RISK IDENT is a young software development company based in Hamburg, Germany

RISK IDENT

By spending the time to follow the steps in this article you should be able to identify over 80% of the risks on your project. Risk = Probability * Impact All projects have risks. Proactively addressing those risks will save you much needed time and energy when you need it most. This is a guide detailing the most useful methods of identifying risks on your software project, regardless of whether you are guided by a Waterfall approach, Agile, Six Sigma, PMI or PRINCE2. Of note: Common risk areas Most of your project risks are likely to arise out of the following prioritised areas (according to a study from ): Tharwon Arnuphaptrairong Misunderstanding of the requirements Lack of commitment and support management Lack of adequate user involvement Failure to gain user commitment Failure to manage end user expectation Changes to requirements Lack of an effective project management methodology This study by lists 70 such risks that you can cross reference, with 745 assigned strategies concerned with risks categorised by Process (56%) and People (44%) Kloppenborg, T. J. & Tesch, D. (2004) Risk Identification Techniques Interview Initial one on one interviews (formal or informal) always highlight project insights beyond the common shared knowledge that should be documented in the Business Case / Project Charter documents. Come prepared to these interviews with leading structured questions and take note of not just the risks, but also of any business drivers, assumptions and constraints (for use in CAA process (below)). Questionnaire This basic questionnaire ( ) below follows to identify risks and work through mitigation strategies. While I find this questionnaire the most useful there are others of note from , an international study by and one from all of which have been nicely documented in a research paper by . full questionnaire Boehm Top 10 Software risks suggested Wallace & Keil Schmidt et. al Han & Huang Tharwon Arnuphaptrairong 1. Personnel shortfalls Are the required number of skilled skills available for the project time frame? Question: Staffing with top talent, job matching, team building, key personnel agreements, cross training Mitigation: 2. Unrealistic schedules and budgets Were all requirements & tasks estimated by the team undertaking the work? Question: Have all dependencies between tasks been identified and accounted for? Question: Detailed milestone cost and schedule estimation, design to cost, incremental development, software reuse, requirements scrubbing Mitigation: 3. Developing the wrong functions and properties Are the requirements for the system clear and understandable and stable? Question: Is there a history of misinterpretation or miscommunication between developers and those defining product requirements? Question: Organizational analysis, mission analysis, operations-concept formulation, user surveys and user participation, prototyping, early users’ manuals Mitigation: 4. Developing the wrong user interface Has the user interface been prototyped and tested on sample user groups? Question: Are user interface goals defined, documented? Question: Prototyping, scenarios, task analysis, user participation Mitigation: 5. Gold plating Is there a history of unneeded functionality being added during project development? Question: Focus on MVP, Requirements scrubbing, prototyping, cost-benefit analysis, designing to cost Mitigation: 6. Continuing stream of requirements changes Has past history with customer shown that requirements change often? Question: Does the team have a good understanding of what the customer/user wants in the system? Question: High change threshold information hiding, incremental development (deferring changes to later increments) Mitigation: 7. Shortfalls in externally furnished components Are there any delivery risks identified with projects to which this one is dependent? Question: Benchmarking, inspections, reference checking, compatibility analysis Mitigation: 8. Shortfalls in externally performed tasks Is this project or any of its tasks dependent upon the completion of external project development efforts? Question: Reference checking, pre-award audits, award-fee contracts, competitive design or prototyping, team building Mitigation: 9. Real-time performance shortfalls Have requirements for system response time been defined? Question: Have requirements for data storage, memory & processing speed been defined? Question: Have user support (number of) requirements been defined? Question: Simulation, benchmarking, modeling, prototyping, instrumentation, tuning Mitigation: 10. Straining computer-science capabilities Has the organization delivered a similar system using similar ? Question: tools Technical analysis, cost-benefit analysis, prototyping, reference checking Mitigation: Brainstorming Gather the various stakeholders into a room to try to generate as many risks as possible. There are no bad suggestions. Initially, you are looking for as many risks to be mentioned as possible (divergent thinking) before focusing in on some risks and building on them (convergent thinking). If the group get stuck, then steer them towards the various project objectives (schedule, scope, quality, budget) and tasks (requirements, testing etc.) You may want to use this to plan your approach to your Brainstorming session and utilise these . link best practices Constraints and Assumptions Analysis (CAA) Every project has known assumptions, documented or not. Every time a requirement, function, task or User Story is sized it is built upon underlying . In order to analyse the work to be undertaken you should note your dependencies and business and technical . The exercise to turn these into risks is straight forward. Simply state that each of these assumptions and constraints are false. This is the Risk. Now describe the consequence. This is the Impact. Whether to deal with the impact or mitigate the risk in the first place will be dealt with in a later article. assumptions constraints An example could be: : The ACME payment solution provider will be used to take payment on the site. Assumption : The ACME payment solution provider will not be used to take payment on the site. Inverted Assumption Why would this have happened? Examples could be that the contract will not be signed in time, or your market currency is not supported. Maybe there are legal issues preventing deployment or ACME’s rates are too high. There are many potential issues (which could be brainstormed) that could arise. Each one is a potential risk. Identifying them early allows you to take the necessary steps to investigate their likelihood and/or take steps to mitigate them. Standard deviation from three point estimates Some practices are just learned through experience. This one is mine. I identify so many potential risks based on this method. There is behind it, but can simply be explained as this: In three point ( ) estimation, if the difference between the smallest number (optimistic) and the largest number (pessimistic) is large, then you have uncovered a risk. This usually happens without the person estimating the task even realising it. complex logic PERT No Risk identified Optimistic estimate = 3 Most Likely Estimate = 5 (+2) Pessimistic estimate: = 7 (+4, +2) Risk identified Optimistic estimate = 3 Most Likely Estimate = 5 (+2) Pessimistic estimate: = 12 (+9, +7) The PERT formula is: in Excel the PERT formula is: (O+P+4×M)/6, =ROUND(((A1+(B1*4)+C1)/6),0) Conditional Formatting screenshot The Standard Deviation (SD) or σ is , in Excel I use this formula coupled with Conditional Formatting to ‘raise a red flag’ if SD is greater than ‘1’: σ = (P — O)/6 =IF((C1-A1)/6<1,””,((C1-A1)/6)) Force Field Analysis (FAA) Your project already has known objectives and driving forces. Weaken the focuses behind them, identify why they may have weakened and identify the impact on the project. How can you mitigate against this? A much more detailed overview of FAA is found . here Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis Identify the strengths and weaknesses of the end-product in the market and translate these all to risks. If you are not convinced to undertake the academic analysis, are some compelling reasons why you should. here Fishbone/ Ishikawa Diagram (Cause and Effect) Diagramming is usually used during Risk Analysis, but introducing this during the Risk Identify stage can help identify root causes as well as various factors leading to the risk. For this reasons the (Cause and Effect) earns its great reputation as a method to determine the root cause of the problem. This can be useful when converging on a risk. Once the root cause has been identified, the risk can be mitigated. Fishbone Diagram Cyclomatic complexity The of a software program indicates how complex the source code is. This method determined the number of independent paths through a program’s source code. It can guide you, post implementation, as to whether or not a program may contain problems. cyclomatic complexity Failure Mode Effects Analysis (FMEA) is a step by step process used to identify possible failures in design, engineering or deployment of a software product. You start by identifying each function of the software and then identify the modes in which it can fail. Later you look to address them. This process requires a cross functional team for best results. FMEA Delphi Technique Having run a Brainstorming session, you may be wondering if using the Delphi Technique is overkill. The answer is no. The reasons are many but key to them is that fact that you have relevant skilled experts in the room, the technique is formal and structured and their feedback is anonymous. To start a Delphi Technique you ask your panel of experts to openly discuss the project, defining the problems. Based on this, provide them with survey or questionnaire to get their anonymous feedback, collate & publish their results, solicit feedback and repeat until you have everything you need. A good step by step guide can be found . Just remember to act on your findings! here Observational gaps in Risk Breakdown Structures RBS as a Pivot Table It is advisable to represent your risks in a (RBS). ( ) This is achieved by categorising risks based on Scope, Schedule, Quality and Cost. I go a step further and create a sub-category related to the major areas / themes within the project. However, I then use this not to observe what is represented in the structure, but rather what is not. If I get the sense that the schedule is the riskiest part of the project, but that is not represented on the RBS, then I know I have gaps. Similarly, if I sense that a certain theme is the riskiest but that is not obvious from the RBS, then I know that I have to spend more time brainstorming on risks related to that Theme. Risk Breakdown Structure I automate this as a Pivot table in Excel. Residual Risks & Secondary Risks Having dealt with original risk, a secondary risk can arise as a direct result of your risk response. You need to cautiously watch out for these as they can sometimes be worse the initial risk. The most cited example is putting out an animal trap in order to protect your home (risk), but a family member gets caught in it instead (secondary risk). If you had caught the wild animal in the trap, this could now attract other unwanted animals, this is a residual risk. Brainstorming should tease out these types of risks. I have also publishing a Risk Identification article, which you may enjoy more as you weren’t planning on spending this much time trying to uncover loads of risks. Next time, we will review the various analysis techniques that can be used to size and prioritise the risks that you have found. less detailed

Everything your competitor knows about Risk Identification on Software projects

By spending just a few minutes to follow the steps in this article you should be able to identify 80% of the risks on your project. All projects have risks. Proactively addressing those risks will save you much needed time and energy when you need it most. But first, lets define the term risk, as it gives us a greater understanding of our later activities: Risk is an uncertain event or condition that, if it occurs, has an effect on at least one [project] objective This statement can be expressed in the following way Risk = Probability * Impact For every risk, we will need to understand the probability or likelihood of it occurring and the impact that it would have on a project, if it were to occur. This is a quick guide, so lets get started in identifying your project risks. Of note: Common risk areas Most of your project risks are likely to arise out of the following prioritised areas (according to a study from ): Tharwon Arnuphaptrairong Misunderstanding of the requirements Lack of t commitment and support managemen Lack of adequate user involvement Failure to gain user commitment Failure to manage end user expectation Changes to requirements Lack of an effective project management methodology Risk Identification Techniques Questionnaire Use this (reduced) questionnaire ( ) to identify risks and work through mitigation strategies, from : full questionnaire suggested Boehm Top 10 Software risks 1. Personnel shortfalls Are the required number of skilled skills available for the project time frame? Question: Staffing with top talent, job matching, team building, key personnel agreements, cross training Mitigation: 2. Unrealistic schedules and budgets Were all requirements & tasks estimated by the team undertaking the work? Question: Have all dependencies between tasks been identified and accounted for? Question: Detailed milestone cost and schedule estimation, design to cost, incremental development, software reuse, requirements scrubbing Mitigation: 3. Developing the wrong functions and properties Are the requirements for the system clear and understandable and stable? Question: Is there a history of misinterpretation or miscommunication between developers and those defining product requirements? Question: : Organizational analysis, mission analysis, operations-concept formulation, user surveys and user participation, prototyping, early users’ manuals Mitigation 4. Developing the wrong user interface : Has the user interface been prototyped and tested on sample user groups? Question : Are user interface goals defined, documented? Question Prototyping, scenarios, task analysis, user participation Mitigation: 5. Gold plating : Is there a history of unneeded functionality being added during project development? Question : Focus on MVP, Requirements scrubbing, prototyping, cost-benefit analysis, designing to cost Mitigation 6. Continuing stream of requirements changes Has past history with customer shown that requirements change often? Question: Does the team have a good understanding of what the customer/user wants in the system? Question: : High change threshold information hiding, incremental development (deferring changes to later increments) Mitigation 7. Shortfalls in externally furnished components Are there any delivery risks identified with projects to which this one is dependent? Question: : Benchmarking, inspections, reference checking, compatibility analysis Mitigation 8. Shortfalls in externally performed tasks Is this project or any of its tasks dependent upon the completion of external project development efforts? Question: : Reference checking, pre-award audits, award-fee contracts, competitive design or prototyping, team building Mitigation 9. Real-time performance shortfalls Have requirements for system response time been defined? Question: Have requirements for data storage, memory & processing speed been defined? Question: Have user support (number of) requirements been defined? Question: Simulation, benchmarking, modeling, prototyping, instrumentation, tuning Mitigation: 10. Straining computer-science capabilities : Has the organization delivered a similar system using similar ? Question tools : Technical analysis, cost-benefit analysis, prototyping, reference checking Mitigation Brainstorming Gather the various stakeholders into a room to try to generate as many risks as possible. There are no bad suggestions. Initially, you are looking for as many risks to be mentioned as possible (divergent thinking) before focusing in on some risks and building on them (convergent thinking). If the group get stuck, then steer them towards the various project objectives (schedule, scope, quality, budget) and tasks (requirements, testing etc.) You may want to use this to plan your approach to your Brainstorming session and utilise the for brainstorming . link best practices Constraints and Assumptions Analysis (CAA) Your project already has known and is built on multiple . Take all of these and invert them or flip them on their head. Now, each one of these should be rewritten as if they failed. You have now identified a material set of risks. For example, if your initial assumption is that your new website will use a certain Payment Provider, then invert this to say that you are now unable to use that Payment Provider. Why would this have happened and what can you do to deal with this? constraints assumptions Standard deviation from Three Point Estimates Some practices are just learned through experience. This one is mine. I identify so many potential risks based on this method. There is behind it, but can simply be explained as this: In three point ( ) estimation, if the difference between the smallest number (optimistic) and the largest number (pessimistic) is large, then you have uncovered a risk. This usually happens without the person estimating the task even realising it. complex logic PERT No Risk identified Optimistic estimate = 3 Most Likely Estimate = 5 (+2) Pessimistic estimate: = 7 (+4, +2) Risk identified Optimistic estimate = 3 Most Likely Estimate = 5 Pessimistic estimate: = 12 (+9, +7) I have also publishing a Risk Identification article for your peers & competitors. You may also enjoy if you wish to dig deeper to uncover more complex risks. Next time, we will review the various analysis techniques that can be used to size and prioritise the risks that you have found. more detailed

Everything you should know about Risk Identification on Software projects

AI Regulations and Standards - ISO/IEC 42001

As businesses grapple with the complexities of global logistics, deploying AI in the supply chain has become a linchpin for those who want to stay ahead of the competition. In this article, we'll underscore the significance of AI in the supply chain and offer insights into a success story that vividly illustrates the tangible benefits of AI in optimizing supply chain management. A supply chain is the entire network of individuals and organizations producing and delivering a product or service to the final consumer. It includes every step, from sourcing raw materials, manufacturing and assembling the product to transportation, warehousing, and finally, reaching the customer's hands. 49% respondents from the supply chain sector expressed that disruptions led to planning difficulties. 44% indicated challenges due to supply chain footprint, which required them to make changes during the year. McKinsey Today, this multifaced network faces many disruptions, such as geopolitical tensions, economic fluctuations, climate change, and evolving regulations. Simultaneously, common goals transcend industries, including customer satisfaction, cost optimization resilience, and adaptation. Both challenges and goals demand innovative solutions, with supply chain optimization emerging as a crucial factor in ensuring efficiency. Brief Overview of Supply Chain Optimization Supply chain optimization (SCO) is a complex field dedicated to managing the flow of goods and services from raw materials to the end customer in the most efficient and cost-effective way possible. SCO is the adjustment of a supply chain's operations that ensures it is at the peak of possible efficiency. Top five challenges of supply chain optimization: Predicting customer demand accurately is crucial for optimizing inventory levels, transportation possibilities, and production schedules. Demand forecasting: Balancing the cost of holding inventory with the risk of stockouts. Inventory management: Selecting the most efficient and cost-effective routes for transporting goods, especially with factors like traffic congestion and fuel prices. Transportation optimization: Finding reliable suppliers who offer competitive prices and quality. Supplier selection and management: Efficient cooperation between all players in the supply chain. Collaboration and communication: To tackle these challenges, SCO employs data analytics, mathematical modeling, optimization algorithms, artificial intelligence (AI), and machine learning (ML). These tools collectively empower businesses to navigate the dynamic landscape of global supply chains with precision. AI and ML, in particular, emerge as powerful allies in the pursuit of supply chain efficiency. Leveraging and machine learning services helps automate tasks, enhance forecasting accuracy, and enable real-time decision-making, ensuring businesses can stay agile and responsive in the face of evolving circumstances. AI development Optimizing Supply Chain Management Through AI Integration AI plays a significant role in improving supply chain efficiency. It can be used in different areas of supply chain management. Enhancing visibility and transparency: Real-time data collection and analysis - Providing real-time insights into inventory levels, shipment locations, and potential disruptions. Predictive analytics - AI can analyze historical data and external factors like weather forecasts or economic trends, potential disruptions, and maintenance needs. Optimizing operations and decision-making: Inventory management - AI can optimize inventory levels by forecasting demand, considering lead times and seasonality patterns, and minimizing costs associated with overstocking or understocking. Route optimization - AI algorithms can determine the most efficient and cost-effective routes for transportation, considering factors like traffic conditions, fuel consumption, and schedules in a way of finding a balance between transportation and inventory costs. Warehouse automation - AI-powered robots can automate tasks like picking, packing, and sorting, improving speed, accuracy, and efficiency in warehouses. Resilience and risk management: Risk identification - AI can analyze data to identify potential disruptions or supply chain vulnerabilities, allowing companies to take pre-emptive measures to minimize their impact. Scenario planning - AI can simulate various scenarios like natural disasters or economic downturns, helping companies develop contingency plans to ensure business continuity. Fraud detection and prevention - AI can detect anomalies in order data and financial transactions, identifying potential fraud attempts, and protecting companies from financial losses. Sustainability and customer experience: Enhanced sustainability - AI can optimize processes to reduce waste, minimize energy consumption, and optimize fuel usage, contributing to a more sustainable supply chain. Personalization and customization - AI can personalize the customer experience by tailoring product recommendations and offerings based on individual preferences and purchase history. Let's explore how AI, in particular, plays a transformative role in reshaping the supply chain in the retail industry, illustrated by a real-world use case. Developing an Intelligent Supply Optimization System Our client, a manufacturer of plumbing tools and equipment with distribution points all around the United States, needed help optimizing their supply process. The main struggle stemmed from the elevated experiences associated with transporting goods from factories in China to distribution centers in the US. The customer maintained numerous warehouses and sales points across almost every state. Thus, determining the optimal routing posed a non-trivial task. In addition, it was crucial to consider the preferred transportation chain. Each product begins its journey in a shipping container from a Chinese seaport and travels the Pacific Ocean to reach a port in the United States, from where it continues its journey in a truck to the designated warehouse. Afterward, the product awaits last-mile delivery to the targeted distribution point. The customer wanted to have the following functionality: specify available warehouses; identify distribution points, with some automatically derived from historical data and others manually selected by the user; create a list of items along with their respective quantities; have a list of seaports. Finally, the client expressed a wish to be able to compare various methods of packing goods within shipping containers, either with or without pallets. The outcome of the optimization problem should yield the optimal transportation schedule and associated expense statistics. But before we dive into use case details, we will quickly outline a fundamental mathematical concept that is used in the intelligent solution. Integer Programming for Supply Chain Efficiency Integer (Linear) Programming (IP) is a mathematical optimization technique used to find the best solution to a problem with linear relationships while considering that either some or all the variables must take integer (whole number) values. The general form of an IP problem is to maximize (or minimize) a linear objective function subject to a set of linear constraints, where some or all the decision variables must be integers. The objective function is a linear combination of decision variables, and the constraints are linear inequalities or equalities that the variables must satisfy. The mathematical formulation of an IP problem can be expressed as follows: Values x₁, x₂, ..., xₙ are the decision variables, c₁, c₂, ..., cₙ are coefficients in the objective function, aᵢⱼ are coefficients in the constraint equations, and bᵢ are constants. Solving an IP problem involves finding the values of the decision variables that optimize the objective function while satisfying the linear constraints and the additional requirement that the variables must be integers. IP problems are commonly used in various fields, including operations research, logistics, manufacturing, finance, and telecommunications, where decision variables often represent quantities that must be whole numbers. Integer Programming is a valuable tool in solving supply chain optimization problems due to its ability to model complex decision-making processes involving discrete (integer) variables. The ways IP can be applied in the context of supply chain optimization: IP can assist in determining optimal inventory levels for different products at various stages of the supply chain, by considering constraints like storage capacity, demand variability, and order quantities, IP models can find the best balance between holding costs and stockouts. Inventory management: IP can optimize production schedules by considering factors such as production capacities, raw material availability, and demand forecasts. The model can help in determining the optimal quantities to produce for each product to meet demand while minimizing costs. Production planning: IP models can be applied to optimize the overall structure of the supply chain network. Routing and scheduling: IP models can be applied to optimize the overall structure of the supply chain network. Supply chain network optimization: IP models can incorporate demand forecasting data to optimize production and distribution decisions while aligning supply chain operations to anticipated customer demand, reducing the risk of overstock or stockouts. Demand planning: Enhancing Supply Chain Logistics – ELEKS’ Solution Overview To meet our client's requirements, we created a system that leverages the benefits of the Integer Programming technique, which optimizes supply schedules, routes, and inventory levels for five kinds of items: ceramic sinks, toilets, bidets, gas valves, and bathtub mixers (corresponding HS codes are 6910.10.0030, 6910.10.0010, 6910.10.0025, 8414.90.1080, 7323.93.0080). The system comprises three main parts (or pages): configuration, simulation, and analytics. Each of them will be explained in the following paragraphs. To ensure the confidentiality of data and application details, we anonymized names and real locations, creating a prototype for a straightforward demo application that illustrates the efficacy of AI in addressing optimization challenges. Configuration Page The user can select available warehouses, distribution points, and desirable seaports to take part in final optimized routing. Also, the client can specify desirable goods and their respective amount to be transported to their customers. The last thing is the date picker, in case delivery is planned. The system will calculate remains inventory at the delivery date based on forecasted demand and allocate planned shipments aligned with warehouse residuals. Clicking the 'optimize' button does all the magic! It sets optimization tasks and launches its solution. After a few seconds, results can be observed on the remaining two pages. Simulation (Map) Page This page offers the client the opportunity to view an accurate simulation of the proposed model's solution. In prior discussions, we deliberated on the suitable INCOTERMS for our client and reached a consensus on CFR. The customer can specify the number of hours for each step and press the corresponding button to see the state of the supply chain in this amount of time. First of all, there are five types of points: two for transport vehicles (ship and truck) and three for seaports (orange), warehouses (purple), and distribution points (green). The map is interactive, meaning the user can hover over any point or connection and see the information related to that object. For example, blue lines connecting warehouses and distribution points show the last-mile delivery plan and contain information about how much each good is delivered daily. If the user hovers over the ship, the information about its capacity, container types, loaded amount of each good, and routing are displayed. Analytics Page This page features all the data and statistics about the model's proposed solution. The user can observe the delivery plan, where each ship and truck are, its estimated arrival time, route, and filling. There are also a bunch of charts, starting from the pie chart with expenses altogether and ending with heat maps of delivery costs between different seaports, warehouses, and distribution points. Additionally, the client can observe a price graph per good for different distribution points and, based on that data, make corresponding business decisions. Performance Metrics: ELEKS’ Solution vs Client’s Legacy System The next challenge was to test our solution in real-world scenarios and compare it to the client's approach. For this purpose, the client considered a period of 1 month. The main conditions of the task (such as available seaports, location, and number of warehouses and clusters) are observed in the illustrations in the previous paragraphs. As a result, our model's solution was more effective regarding operating expenses, average time of last-mile delivery, and reduced idle time of warehouses. 5.76% average monthly cost savings achieved by the client and 50% reduction in delivery time for some clusters. ELEKS Data Science team The data indicates that the utilization of our solution proved to be more advantageous for the client, resulting in a reduction in the price per item. This outcome can be attributed to various factors, with the primary ones being as follows: More intelligent goods distribution More optimal delivery network More efficient routing and warehousing For example, our solution featured some clusters for which the model suggests last-mile delivery for different goods from distinct warehouses (an explanation lies deeply in the delivery network, and our model spotted this optimization possibility). The distribution of goods between warehouses differed (mainly between warehouses in Washington and New York). At first sight, the differences are not so significant, but the result speaks for itself. Conclusions The integration of artificial intelligence (AI) in supply chain management emerges as a critical component for businesses aiming to navigate the complexities of global logistics effectively. The multifaceted nature of modern supply chains, coupled with challenges such as disruptions, geopolitical tensions, and economic fluctuations, necessitates innovative solutions for achieving efficiency and resilience. The presented solution highlights AI's transformative role in streamlining supply chain managers' responsibilities and demonstrates the tangible benefits of AI and IP in supply chain logistics, showcasing a 5.76% average monthly cost savings, a 50% reduction in delivery time for some clusters. Its adaptability allows seamless adjustments, offering users access to a simulation studio, where they can execute various scenarios involving diverse items and quantities, facilitating a comparative analysis to determine optimal strategies for different situations. To schedule a demo of this transformative solution firsthand and explore its capabilities further, . reach out to us

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Austin Native Zak Westphal Finds Success in High-Risk Trading Zak Westphal was born and raised in Austin, Texas. From a young age, he was fascinated by the stock market and dreamed of becoming a trader. After graduating from the University of Texas at Austin with a degree in finance, Westphal moved to New York City to pursue his passion. "Growing up in Austin, I was always interested in business and trying to make money through entrepreneurial ventures," says Westphal. "Moving to New York gave me the opportunity to really dive into the world of trading and finance on Wall Street." Westphal got his start as a proprietary trader at a small brokerage firm. Working long hours analyzing charts and market data, he and developed a knack for identifying lucrative opportunities in volatile sectors like cryptocurrencies and penny stocks. refined his trading strategies Within a few years, Westphal's account had grown significantly through consistently profitable trades. He proved himself as a talented trader capable of producing strong returns despite taking on high risks. His success caught the attention of investment firms looking to bring on young talent. Building a Platform for Other Traders In 2012, Westphal decided to branch out on his own and launch an educational platform called StocksToTrade to help other retail traders navigate risky markets. His goal was to provide the tools and knowledge he wished he had when first starting out. "I saw a need to educate traders and give them a level playing field to implement the same strategies as professionals," explains Westphal. "Too many people were still losing money just learning the basics trial by error." StocksToTrade quickly grew a loyal following with its extensive library of trading courses, real-time market scanning tools, and mentorship programs. Members gained unprecedented access to Westphal's daily insights and strategies. He shared his approach for managing risk, identifying patterns, and exploiting volatility—all skills he had developed over a successful decade trading. Today, StocksToTrade has over 500,000 users in more than 100 countries. It has become one of the most popular online destinations for traders globally. Westphal's engaging educational content and high profile as a top trader have earned him recognition as a leading expert. Applying a Disciplined Mindset Despite the potential for massive rewards in his fields, Westphal acknowledges that trading volatile assets also opens the door to outsized losses if not done responsibly. He credits his success to cultivating an objective, able to withstand emotional swings. data-driven mindset "What separates consistently profitable traders is discipline—having a clear strategy and sticking to it without getting swept up in fear or greed," says Westphal. "You need to look past the noise and focus only on high-probability setups defined by hard numbers, not opinions." Westphal's approach centers on strict risk management, position sizing according to account size, and methodically optimizing strategies based on backtesting. He emphasizes the importance of paper trading to prove out concepts in different market environments before risking capital. This disciplined process has allowed Westphal to profit from speculative sectors that chew up most traders who lack his experience and temperament. It's also what has enabled him to build a profitable business educating others on his framework. Continuing to Evolve Strategies Even after over a decade in the markets, Westphal still devotes considerable time and resources to enhancing his trading system. He recognizes that strategies must evolve along with changing market dynamics to maintain an edge. "Complacency is what kills most traders. You have to constantly challenge your assumptions and analyze what you've been doing right or wrong," says Westphal. Through rigorous backtesting and review of past trades, Westphal looks for subtle shifts in factors like volatility levels, sector rotations, or macroeconomic conditions that may require tweaking indicators. He also experiments with new algorithms and screening methods with the goal of optimizing performance. This is what Westphal believes separates consistently top traders from those who have temporary success. It also drives his mission to provide traders with the latest techniques through StocksToTrade's continuous education offerings. growth mindset Expanding the Platform Globally In recent years, Westphal has focused on expanding StocksToTrade's international reach. He saw an opportunity to help traders worldwide gain the same knowledge and tools available primarily to North Americans. "Markets are global but educational resources weren't always accessible equally everywhere," Westphal notes. "I wanted to level the playing field and connect traders globally to learn from each other." StocksToTrade now provides educational content in over 15 languages. Westphal has also added localized mentoring programs and social communities for international traders. The platform's global analytics and scanning tools give members a unified view into opportunities worldwide. This expansion has paid off, with over half of StocksToTrade's 500,000+ users now based outside of the United States. Westphal is especially passionate about helping traders in developing regions gain skills to find success through responsible volatility trading. Full Circle to Giving Back After achieving great success through trading and his educational business, Westphal has come full circle by focusing on empowering the next generation. He established a nonprofit foundation that provides free trading courses and scholarships specifically for students. "I owe so much of where I am to those who shared their wisdom freely. It's gratifying to help young people from all backgrounds gain practical skills and change their trajectories like this industry did for me," says Westphal. Through passionate dedication to education, Westphal continues cultivating talented new traders worldwide. He hopes his story serves as inspiration that volatile markets can become vehicles for achievement when navigated responsibly. Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure:

Revolutionizing Trading Education with Zak Westphal

জাক ওয়েস্টফালের সাথে ট্রেডিং শিক্ষার বিপ্লব

Revolutionierung der Trading-Ausbildung mit Zak Westphal

Revolucionando la educación comercial con Zak Westphal

Révolutionner l'éducation au trading avec Zak Westphal

जैक वेस्टफाल के साथ व्यापारिक शिक्षा में क्रांति लाना

ザック・ウェストファルによるトレーディング教育に革命を起こす

Zak Westphal과 함께하는 트레이딩 교육 혁신

Revolucionando a educação comercial com Zak Westphal

Революция в торговом образовании с Заком Вестфалем

Zak Westphal ile Ticaret Eğitiminde Devrim Yaratıyor

Cách mạng hóa giáo dục giao dịch với Zak Westphal

扎克·韦斯特法尔 (Zak Westphal) 彻底改变交易教育

Financial fraud prevention is a race against time. Implementation-wise, it relies heavily on the data processing power, especially under large datasets. Today I'm going to share with you the use case of a retail bank with over 650 million individual customers. They have compared analytics components including , ClickHouse, Greenplum, Cassandra, and Kylin. After 5 rounds of deployment and comparsion based on 89 custom test cases, they settled on Apache Doris, because they witnessed a six-fold writing speed and faster multi-table joins in Apache Doris as compared to the mighty ClickHouse. Apache Doris I will get into details about how the bank builds their fraud risk management platform based on Apache Doris and how it performs. Fraud Risk Management Platform In this platform, return results in less than and of them are finished in under On average, the solution every day and for bank customers. 80% of ad-hoc queries 2 seconds, 95% 5 seconds. intercepts tens of thousands of suspicious transactions avoids losses of millions of dollars This is an overview of the entire platform from an architectural perspective. The can be roughly categorized as: source data Dimension data: mostly stored in PostgreSQL Real-time transaction data: decoupled from various external systems via Kafka message queues Offline data: directly ingested from external systems to Hive, making data reconciliation easy For , this is how they collect the three types of source data. First of all, they leverage the to to synchronize metadata and user data from PostgreSQL. data ingestion JDBC Catalog The transaction data needs to be combined with dimension data for further analysis. Thus, they employ a Flink SQL API to read dimension data from PostgreSQL, and real-time transaction data from Kafka. Then, in Flink, they do multi-stream joins and generate wide tables. For real-time refreshing of dimension tables, they use a Lookup Join mechanism, which dynamically looks up and refreshes dimension data when processing data streams. They also utilize Java UDFs to serve their specific needs in ETL. After that, they write the data into Apache Doris via the . Flink-Doris-Connector The offline data is cleaned, transformed, and written into Hive, Kafka, and PostgreSQL, for which Doris creates catalogs as mappings, based on its capability, to facilitate federated analysis. In this process, Hive Metastore is in place to access and refresh data from Hive automatically. Multi-Catalog In terms of , they use Apache Doris as a data warehouse and apply different for different layers. Each layer aggregates or rolls up data from the previous layer at a coarser granularity. Eventually, it produces a highly aggregated Rollup or Materialized View. data modeling data models Now let me show you what analytics tasks are running on this platform. Based on the scale of monitoring and human involvement, these tasks can be divided into real-time risk reporting, multi-dimensional analysis, federated queries, and auto alerting. Real-time risk report When it comes to fraud prevention, what is diminishing the effectiveness of your anti-fraud efforts? It is incomplete exposure of potential risks and untimely risk identification. That's why people always want real-time, full-scale monitoring and reporting. The bank's solution to that is built on Apache Flink and Apache Doris. First of all, they put together the 17 dimensions. After cleaning, aggregation, and other computations, they visualize the data on a real-time dashboard. As for , it analyzes the workflows of . scale over 10 million customers, 30,000 clerks, 10,000 branches, and 1000 products As for , the bank now has evolved from next-day data refreshing to near real-time data processing. Targeted analysis can be done within minutes instead of hours. The solution also supports complicated ad-hoc queries to capture underlying risks by monitoring how the data models and rules run. speed Multi-dimensional analysis to identify risks Case tracing is another common anti-fraud practice. The bank has a fraud model library. Based on the fraud models, they analyze the risks of each transaction and visualize the results in near real time, so their staff can take prompt measures if needed. For that purpose, they use Apache Doris for of cases. They check the patterns of transactions, including sources, types, and time, for a comprehensive overview. During this process, they often need to combine of different dimensions. This is empowered by the capabilities of Apache Doris. Both rule-based matching and list-based matching of cases can be done without manual efforts. multi-dimensional analysis over 10 filtering conditions ad-hoc query within seconds Federated queries to locate risk details Apart from identifying risks from each transaction, the bank also receives risk reports from customers. In these cases, the corresponding transaction will be labeled as "risky", and it will be categorized and recorded in the ticketing system. The labels make sure that the high-risk transactions are promptly attended to. One problem is that, the ticketing system is overloaded with such data, so it is not able to directly present all the details of the risky transactions. What needs to be done is to relate the tickets to the transaction details so the bank staff can locate the actual risks. How is that implemented? Every day, Apache Doris traverses the incremental tickets and the basic information table to get the ticket IDs, and then it relates the ticket IDs to the dimension data stored in itself. At the end, the ticket details are presented at the frontend of Doris. This entire process takes . This is a big game change compared to the old time when they had to manually look up the suspicious transaction. only a few minutes Auto alerting Based on Apache Doris, the bank designs their own alerting rules, models, and strategies. The system monitors how everything runs. Once it detects a situation that matches the alert rules, it will trigger an alarm. They have also established a real-time feedback mechanism for the alerting rules, so if a newly added rule causes any negative effects, it will be adjusted or removed rapidly. So far, the bank has added nearly 100 alerting rules for various risk types to the system. During the past two months, were issued with in less than after the risk situation arises. over 100 alarms over 95% accuracy 5 seconds Conclusion For a comprehensive anti-fraud solution, the bank conducts full-scale real-time monitoring and reporting for all their data workflows. Then, for each transaction, they look into the multiple dimensions of it to identify risks. For the suspicious transactions reported by the bank customers, they perform federated queries to retrieve the full details of them. Also, an auto alerting mechanism is always on patrol to safeguard the whole system. These are the various types of analytic workloads in this solution. The implementation of them rely on the capabilities of Apache Doris, which is a data warehouse designed to be an all-in-one platform for various workloads. If you try to build your own anti-fraud solution, the are happy to exchange ideas with you. Apache Doris open source developers Also appears . here

Financial Anti-Fraud Solutions Available on the Apache Doris Data Warehouse

In this interview, we will explore the nuances of risk adjustment and how it differs from traditional investment strategies. We will uncover how risk adjustment can effectively mitigate the negative impacts of a bear market on investments, safeguarding portfolios against volatility and market downturns. We will also look at the primary risks associated with risk adjustment during bear markets and share valuable insights on how investors can manage and mitigate these risks effectively. Moreover, we will discuss the advantages that risk adjustment techniques offer in a bear market scenario, highlighting specific examples of how they can open up opportunities for investors. Please tell us your name and what you do. I'm Steve Rosenblum, founder of Libertify, the AI-powered risk management solution for retail investors, enabling anyone to manage risk effortlessly. We provide innovation portfolio management through artificial intelligence-powered insights. Prior to launching my first Web3 enterprise, I focused on a major motto: “Empower everybody’s potential.” I founded Pixmania, which went on to earn €1 billion in revenue, Deezer and Molotov., as well as the Dotcorp Finance Private Equity Fund. How does risk adjustment differ from traditional investment strategies, and how can it mitigate the impact of a bear market on investments? Risk adjustment differs from traditional investment strategies in that it focuses on optimizing portfolio performance while considering the level of risk undertaken. Unlike traditional strategies that primarily emphasize maximizing returns, risk adjustment takes into account factors such as volatility, correlation, and downside risk. By incorporating these elements, risk adjustment aims to create a more resilient portfolio that can better withstand adverse market conditions. In the context of a bear market, risk adjustment can mitigate the impact on investments through several mechanisms. Firstly, it involves diversification across various asset classes, industries, and geographic regions, reducing the concentration risk inherent in traditional strategies. Secondly, risk-adjusted portfolios may include assets that exhibit a lower correlation with the broader market, thereby potentially buffering the portfolio against severe market declines. Additionally, risk adjustment often employs dynamic asset allocation techniques that respond to changing market conditions, allowing for timely adjustments in response to a bear market's evolving dynamics. In your opinion, what are the primary risks associated with employing risk adjustment strategies during a bear market, and how can investors manage or mitigate these risks effectively? In my opinion, the primary risks associated with employing risk adjustment strategies during a bear market revolve around the potential for over-optimization and unintended consequences. Overly complex risk adjustment models may lead to excessive trading and transaction costs, eroding returns. Moreover, these strategies could inadvertently increase exposure to certain risk factors that perform poorly during specific market downturns. To manage or mitigate these risks effectively, investors should prioritize transparency and simplicity in their risk adjustment approaches. Regular stress-testing of models against historical bear market scenarios can help validate their effectiveness. Additionally, a robust risk management framework that incorporates continuous monitoring and reassessment of the chosen strategies is essential. Maintaining a diversified portfolio, periodically rebalancing, and considering a broad range of risk factors can collectively contribute to a more resilient risk-adjusted investment approach during bear markets. Please explain the role of AI in risk management. AI risk management tailored for retail investors in the cryptocurrency market is a transformative concept. It provides useful insights, analysis, and automation to help users navigate the volatile and changing nature of digital assets. It can also assist investors in making informed decisions, optimizing their portfolios, and mitigating the risks involved with cryptocurrency investing. It's time to draw lessons from the past after the recent bear market. For example, by auditing your portfolio with AI, you can get a unique way to scan risk exposure. The optimal risk and reward strategies can be determined by using artificial intelligence, which can operate as a 24/7 perpetual coach for users to optimize their existing position. In the context of the bear market, what are the main advantages of employing risk adjustment techniques? Can you provide some examples of how it can open up opportunities for investors? In the context of a bear market, employing risk adjustment techniques offers the advantage of enhanced downside protection and improved risk-adjusted returns. By incorporating strategies that consider factors such as volatility and correlation, investors can potentially reduce portfolio losses and mitigate the impact of market downturns. For example, allocating a portion of the portfolio to assets with low correlation to the broader market, such as gold or certain defensive stocks, can help stabilize returns during bearish periods. Furthermore, dynamic asset allocation based on evolving market conditions can enable investors to capitalize on opportunities arising from changing risk profiles and asset valuations, enhancing the overall resilience and potential gains of the portfolio. When implementing risk adjustment strategies, what factors should investors consider to ensure they capitalize on opportunities during a bear market? When implementing risk adjustment strategies, investors should consider factors such as asset diversification, correlation dynamics, historical stress testing, and adaptive asset allocation. These factors collectively enable investors to navigate a bear market by reducing concentration risk, identifying assets with low correlation to market downturns, validating strategies against historical adverse scenarios, and adjusting allocations based on evolving market conditions. With the potential increase in market volatility during a bear market, how can risk adjustment assist in preserving capital and reducing overall portfolio risk? During a bear market, risk adjustment can aid in capital preservation and reducing portfolio risk by emphasizing strategies that prioritize asset diversification, incorporating low-correlation assets, and implementing dynamic asset allocation. These approaches help mitigate the impact of heightened market volatility, reducing the overall vulnerability of the portfolio to severe losses and providing a more resilient foundation for preserving capital in challenging market conditions. Are there specific industries or sectors that tend to offer more opportunities for investors during a bear market, and how does risk adjustment play a role in optimizing exposure to these opportunities? Certain industries or sectors that exhibit defensive characteristics, such as utilities, healthcare, and consumer staples, often offer opportunities during bear markets. Risk adjustment plays a crucial role in optimizing exposure to these opportunities by identifying assets with low correlation to broader market declines. By incorporating such assets into a diversified portfolio and dynamically adjusting allocations based on market conditions, risk adjustment strategies can enhance the portfolio's resilience and potential for capitalizing on the unique strengths of defensive industries, thus mitigating overall risk during bear markets. What are some of the key quantitative and qualitative indicators that investors should focus on when employing risk adjustment strategies in the context of a bear market? Investors should focus on quantitative indicators such as historical volatility, correlation coefficients, and downside risk measures like Value at Risk (VaR) when employing risk adjustment strategies in a bear market. Qualitative indicators include fundamental analysis of company financials, industry stability, and macroeconomic factors. Integrating both quantitative and qualitative insights enables a comprehensive risk assessment, aiding investors in constructing resilient portfolios during bear markets. How do regulatory changes and macroeconomic factors influence the effectiveness of risk adjustment strategies during a bear market? What steps should investors take to stay informed about these potential impacts? Regulatory changes and macroeconomic factors significantly influence risk adjustment strategies' effectiveness in a bear market. Changes in regulations can alter market dynamics and risk exposures, impacting portfolio performance. Macroeconomic shifts like interest rate changes or fiscal policies can affect asset correlations and overall market sentiment, influencing risk-adjusted returns. Investors should stay informed by regularly monitoring regulatory updates, economic indicators, and policy shifts. Engaging with industry experts, utilizing financial news sources, and participating in professional networks can provide insights into potential impacts. Additionally, employing scenario analysis and stress testing within risk models can help anticipate how regulatory changes and macroeconomic shifts might affect portfolio performance, aiding in informed decision-making during bear markets. As the economic landscape evolves, new investment products and financial instruments may emerge. How can risk adjustment accommodate these innovations and identify opportunities within them during a bear market? Risk adjustment can accommodate new investment products and financial instruments by adopting a flexible and adaptive framework. It involves assessing the risk-return profiles of these innovations, considering their correlation to existing assets, and incorporating them into the portfolio strategically. By continuously updating quantitative models and stress tests to include these new instruments, investors can identify opportunities that align with their risk-adjusted strategies, enhancing the portfolio's resilience and potential gains during a bear market. Regular monitoring and reassessment of these innovations within the risk adjustment framework are key to optimizing their integration and impact.

How Can Risk Adjustment Open Up Opportunities Over The Bear Market?

Every SaaS provider goes through the review process before the integration phase with a large or medium-scale business. This is especially important for American corporations but is also widely accepted worldwide. had its own experience with the SOC2 certification while working with . WebLab Technology Shaman BV Why not ISO 27001? ISO 27001 is a standard for ensuring proper management of digital assets such as financial information, intellectual property, employee details, or information entrusted by third parties. SOC 2 is more widely accepted and is usually preferred by US and Canadian companies. Also, I find it helpful that SOC is split into SOC 1 and SOC 2. First stands for financial control. So, SaaS Providers could focus only on SOC 2. During a SOC 2 certification, a company is evaluated against a set of trust service categories that provide the backbone of the assessment: . Systems and the data stored are protected against unauthorized access and disclosure. Security . Information and systems are available for operation and use. Availability . Confidential information is protected. Confidentiality System processing is complete, valid, accurate, timely, and authorized. Customer data remains correct throughout data processing. Processing integrity. . Personal information is collected, used, retained, disclosed, and disposed of by pre-stated policies. Privacy SOC 2 Technical Components It’s important to understand that SOC 2 is not only about security. Data managed by SaaS providers could be crucial for businesses as a whole. Depending on the integration depth, even a short-term outage might lead to considerable financial or reputational losses. That’s why it is essential to ensure that a SaaS provider maintains best practices. SOC 2 consists of numerous control points, for example: Host-based vulnerability scans are performed at least quarterly on all external-facing systems. Critical and high vulnerabilities are tracked to remediation. The company utilizes a log management tool to identify events that may potentially impact the company’s ability to achieve its security objectives. The company’s penetration testing is performed at least annually. A remediation plan is developed, and changes are implemented to remediate vulnerabilities in accordance with SLAs. And so on. In total, at the moment, there are 235 similar technical control points. Control point statuses should be regularly updated and maintained. SOC 2 Type 1 & 2 SOC 2 is represented in Type 1 and Type 2. Type 1 stands for a point-in-time audit on a date chosen with the audit request. It is reasonable to plan for at least six months to prepare for Type 1. Type 2 shows how controls operate over a period of time, usually 6–12 months after Type 1. During this process, the auditor goes through all control points in the historical view. However, suppose there are misses for control points for a substantial period. Then, even if it successfully passes control points at this very moment, it is possible that the auditor would request an additional 6–12 months. Consistency is required. SOC 2 is not about technical points SOC 2 has numerous administrative principles: Management: The entity identifies risks. The entity internally communicates information, including objectives and responsibilities for internal control. The entity communicates with external parties, including clear risk identification. Board of directors: The board of directors demonstrates independence from management. Management establishes, with board oversight, structures and reporting lines. HR: The entity demonstrates a commitment to attracting, developing, and retaining competent individuals in alignment with its objectives. The entity holds individuals accountable for their internal control responsibilities. The list of principles is not complete. These principles reduce to numerous more concrete control points. Apart from a strong team, you need tooling and processes. It is not one-time work — it is constant maintenance. We should ensure that change and control are not just something that is only talked about. There is an excellent quote from. We had to introduce dozens of policies, improve architecture and introduce numerous monitoring solutions like , , , and . Trendmicro Conformity Wazuh Sentry DataDog We prefer to work with AWS, so we have also been using a dozen of their tools, like , , , , , etc. AWS Inspector AWS WAF AWS KMS AWS Secrets Manager AWS CloudTrail But the biggest game changer is . They provided us with a better preparation framework and continuous monitoring of security and compliance. Vanta We also find the a good starting point, even if SOC2 is not yet required. Operational, Security, Reliability, and Sustainability pillars heavily overlap with what is expected in SOC2. AWS Well-Architected Framework And finally — how to set up for success? Like with many other things, you need Team, Approach, and Process. Hire a skilled R&D team with DevOps and SysOps. A subteam should be allocated to be responsible for preparing for SOC 2 with clear leadership. Get a security consultant. It is better to choose an external consultant or a company. For most SaaS companies, having a full-time security specialist on the team is overkill. Have clear timelines. Expect 6–12 months for Type 1 and another 6–12 months for Type 2. Start early, especially with documentation, policies, and architectural changes. Request Penetration testing early. Integrate frameworks and compliance monitoring tools. Audit time SOC auditors are independent certified public accountants (CPA) who work with the SOC suite to evaluate and report on the controls at a service organization. It is worth looking at , , . We in WebLab had been working with the first one. Sensiba San Filippo Seiler Hood & Strong There are numerous CPAs on the market. Suggested selection criteria: . It is wise to have a meeting with CPAs while choosing one. Even if they are highly professional, communication with some CPAs is smoother than with others. Communication . Some CPAs might not be available during the period you need. Timelines . If integration happens based on the request received from the client, they might also have some expectations. Client preference An audit for Type 1 takes a few weeks, usually up to 5. Good luck! About the author __ __is a Solution Architect and open-source believer with a big passion for Software Development. Oleksandr Knyga Oleksandr lives the life of a digital nomad. When not tapping on the keyboard, he can be found hiking, snowboarding, fishing, and exploring the city. Oleksandr has an MBA and a Computer Science Master’s degree. Also published . here

How to Manage Your SOC 2 Certification and Be Successful

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