Best match

Most recent

The Federal Reserve is set to make an announcement on rates at 2 pm ET. Markets are expecting another pause in the central bank's rate-hiking campaign as the economy continues to absorb the effects of higher borrowing costs.

https://www.bing.com/th?id=OVFT.vZcQzBFYVCi_QtS0K9Un9C&pid=News

Markets await latest Federal Reserve decision on rates

https://www.bing.com/th?id=ODF.t0BGYosS4GKZSebCajKQGA&pid=news

JPMorgan Chase

Bank

Seeking Alpha

JPMorgan Chase is the leading commercial bank and enjoys a wide economic moat and has shown strong financial performance since 2008. The bank's diverse portfolio of financial services and massive scale give it a competitive advantage over its peers.

https://www.bing.com/th?id=OVFT.NZeA0RnyhW6DCAACgW8aNi&pid=News

JPMorgan Chase: A Deep-Dive Analysis Into The Banking Giant

JPMorgan Chase has partnered with numerous nonprofits in Colorado through the years to help underserved entrepreneurs and small businesses.

https://www.bing.com/th?id=OVFT.Jjs4hGpUjdna7yMnk4dQXS&pid=News

JPMorgan Chase supports initiatives that provide coaching, resources to Colorado diverse business owners

https://www.bing.com/th?id=ODF.t3F4eXZUTDaqQci8YlUgMw&pid=news

The Business Journals

For the past 20 years, JPMorgan Chase has retained the top spot as the largest US bank. In its latest release, based on data as of June 30, the Fed ranked 2,113 US commercial banks with consolidated assets of $300 million or more. Read on to see how the 15 biggest banks in the US stack up today.

https://www.bing.com/th?id=OVFT.1FBxvyB7AwxzsKcpIm-Vvy&pid=News

The 15 Biggest Banks in the US

https://www.bing.com/th?id=ODF.vS3pKUrPWgIKkQBHtUkEPQ&pid=news

CNET

Earnings

Slump

The dollar rose against other major currencies during the third quarter, eroding revenues at large companies such as Pfizer and Exxon Mobil, FactSet said.

Companies relying on foreign sales see Q3 earnings slump 4.7%

cfodive

The Carolinas

Charlotte

With this year bringing its share of notable events impacting the economy, the outlook among business leaders has ... industry veteran Tara Coffey, JPMorgan Chase's Carolinas region manager for middle market banking, to ask for her thoughts on how these ...

https://www.bing.com/th?id=OVFT.dJGDc84RE2YW0QsE0GCDmS&pid=News

JPMorgan Chase’s Carolinas Middle Market Banking Head on Charlotte’s thriving market, economic uncertainties and AI

CSRWire USA

KeyBank

Native Americans in the United States

KeyBanc Capital Markets appoints Ben Rechkemmer to lead its Native American Financial Services (NASF) business. NAFS works with more than 200 tribes and tribal enterprises across the country.

https://www.bing.com/th?id=OVFT.lwoPp4rS-G2VymovnEufwy&pid=News

KeyBanc Capital Markets Appoints Ben Rechkemmer To Lead Native American Financial Services

https://www.bing.com/th?id=ODF.hEqYQrIu_oVxEcF1aiBtsQ&pid=news

CSR Wire

Bank of America

The Motley Fool

The other three important divisions are wealth and investment management, commercial banking, and global markets. Combined, they accounted for the remainder of Bank of America's revenue in the quarter.

https://www.bing.com/th?id=OVFT.BvYWAXrhWSEJtoCXgjPhlS&pid=News

JavaScript

4 Things You Need to Know if You Buy Bank of America Today

https://www.bing.com/th?id=ODF.4QjIDUvHz3Rc67mtMVQu7A&pid=news

South Carolina

Hull

Also, Boeing Co.'s CEO claps back at a recent news report that he works a lot from his South Carolina and New Hampshire homes.

https://www.bing.com/th?id=OVFT.UdGXq02oA0YubAM0lrZqHC&pid=News

A new tour of duty: SC-based submarine hull's former name resurfaces

https://www.bing.com/th?id=ODF.70-LnngeSG1sW2m_LvztaA&pid=news

Post and Courier

Technology

Tradition

Welcome to the digital age, where innovation is at the forefront of transforming industries across the globe. One sector that has experienced a major upheaval is banking. Gone are the days when we had to stand in long queues or wait for weeks to get our financial transactions processed.

https://www.bing.com/th?id=OVFT.x1o_5JqM071aUoh2SNm8yS&pid=News

Harnessing the Digital Revolution: How Financial Technology Companies are Disrupting Traditional Banking

https://www.bing.com/th?id=ODF.1DZ_JhFp0wvfm_WCXhrgng&pid=news

Impacts

Get the most recent info and news about JPMorgan Chase on HackerNoon, where 10k+ technologists publish stories for 4M+ monthly readers.

JPMorgan Chase & Co. operates as a financial services company worldwide. It operates in four segments: Consumer & Community Banking (CCB), Corporate & Investment Bank (CIB), Commercial Banking (CB), and Asset & Wealth Management (AWM). The CCB segment offers deposit and investment products and services to consumers; lending, deposit, and cash management and payment solutions to small businesses; mortgage origination and servicing activities; residential mortgages and home equity loans; and credit card, payment processing, auto loan, and leasing services. The CIB segment provides investment banking products and services, including corporate strategy and structure advisory, and equity and debt markets capital-raising services, as well as loan origination and syndication; cash management and liquidity solutions; and cash securities and derivative instruments, risk management solutions, prime brokerage, and research. This segment also offers securities services, including custody, fund accounting and administration, and securities lending products for asset managers, insurance companies, and public and private investment funds. The CB segment provides financial solutions, including lending, treasury, investment banking, and asset management to small business, large and midsized corporations, local governments, and nonprofit clients; and commercial real estate banking services to investors, developers, and owners of multifamily, as well as to office, retail, industrial, and affordable housing properties. The AWM segment offers investment and wealth management services across equities, fixed income, alternatives, and money market fund asset classes; multi-asset investment management services; retirement products and services; and brokerage and banking services. The company also provides ATM, online and mobile, and telephone banking services. The company was founded in 1799 and is headquartered in New York, New York.

JPMC published a in 2021 highlighting their approach to . This article summarizes the problem statement, solution, and other key technical components of the paper* paper Entity Linking What is Entity Linking? It's the task of assigning a to ambiguous mentions of in a text. unique identity named entities Here, “ ” from the text is given a unique identity via a URL (the most common type of ) “ ”. Note that the type of URI used to identify the mentioned entity depends on the domain uniquely. For eg, Instead of a web address, we could have used ISBNs if we were to identify books from a text. Paris URI wikipedia.org/wiki/Paris JPMC was interested in : from to the entities stored in their internal knowledge base (stored as a Mapping mentions of financial institutions news articles Knowledge Graph) An example is shown below: There are two sub-problems that must be defined: : Recognition Extraction of mentions from financial news articles. JPMC has used for this. Spacy : Linking Choosing the correct entity from the internal Knowledge Graph to be linked to the extracted mentions in the previous step. The paper discusses this step. A pictorial representation of this is shown below: String Matching These approaches capture the “morphological” structure of the entity names. The team experimented with (a) Jaccard (b) Levenshtein (c) (also known as ) Ratcliff-Obershelp Gestalt-Pattern-Matching (d) **Jaro Winkler**(e) N-Gram Cosine Similarity The con of these approaches is that they focus just on the “ ” of the names and not the semantics. An example of a failure case would be the matching of “Lumier” and “Lumier”. Even though they are exactly the same, they refer to two different entities. syntactics 2. : Context Similarity Methods These methods take the contexts around mentions and entities to give a similarity score. The Context for “mention” is text to the left and right of the mention, whereas The Context for “entity” is all the data that's stored in the KG for this entity. Finally, Cosine similarity / Jaccard similarity can be used on top of the context vectors. 3. : ML Classification Naive Bayes, Logistic Regression, and SVM are trained on (mention, entity) pairs to find the ones that should be linked 4. Learn to Rank Methods (LTR) These models work in tandem with ML approaches, which might give us multiple (mention, entity) pairs as the solutions. LTR approaches just narrow down to the most probable solution. The idea is to capture both (the meaning that the or stands for) and (character composition of the name) between the names and use a contrastive loss function to train a model. Semantic distance mention entity Syntactic distance We will see below how both of these distances are calculated step by step. Step 1: Obtain embeddings for Entities and Mentions To come up with both of the distances, the authors have proposed to use embeddings for as well as in the KG. mentions entities To obtain , the authors have used a (shown below) embeddings Entity Triplet Loss function For each entity, they used 10 positive and 10 negative samples, making . 10 <entity, positive word, negative word> triplets Unlike embeddings, which they had precomputed, were trained using the on-the-go embeddings approach, where the embedding matrix is learned during the training itself. Entity the embeddings mention Step 2: Calculate Syntactic Distance score Before going further, it's worth mentioning which was introduced by Google in 2016. You can find their official blog . We won’t go into the details, but to give a summary, it's an architecture that has two components — The Wide component and the Deep component. “Wide & Deep” architecture here score calculation is done using the WIDE part, which consists of a Syntactic Distance Linear Siamese Network. The output of the siamese network is the vectors for both the and the which are then compared to find the Euclidean distance. entity mention, Step 3: Calculate Semantic Distance score score calculation is done using the DEEP part Semantic Distance is the pre-trained embedding for the “Apache Corp” that was calculated in Step 1. To obtain the embedding for the , its left and right context words are fed into a Bi-LSTM network that trains the embeddings. The embedding vectors of and the ( are then used to find the Euclidean distance: eₖ mention mention (Vₘ) entity Vₑ) Step 4: Compute Contrastive Loss Both Syntactic and Semantic distances are combined in a weighted fashion as follows: The contrastive loss function is then combined as follows: Contrastive Loss Function where Y is the ground truth value, where a value of 1 indicates that mention m and entity e are matched, 0 otherwise. Combining all the pieces, the final model framework is shown below: At the time of writing this paper, JPMC was still in the process of deploying the model, which, once done, will help support users across JPMC in discovering relevant and curated news that matters to their business. From the cost perspective, not all the mentions are fed through the JEL framework as that would be computationally expensive. JPMC has put another blocking layer to funnel out the mentions that share less than 2 bigrams with the entities from their internal KGs. Once again, is the paper link if you would like to read the full paper. here Also published here Thanks for reading !! Follow Intuitive Shorts (my Substack newsletter), to read quick and intuitive summaries of ML/NLP/DS concepts. You can also follow me on . Medium

Neural Entity Linking and How JPMorgan Chase Plans to Use it

Continuous Integration and Continuous Deployment (CI/CD) have become necessary practices in this world where technology is moving fast, but security remains a top concern. CI/CD pipelines are the core of software delivery, but they can also show great weakness when it comes to security. With how complex software development is growing, it is important to have measures that will ensure the safety of . This is where AIOps comes into play, as the secret weapon for CI/CD security. CI/CD pipelines The State of CI/CD Security Understanding the current situation is crucial. CI/CD pipelines face a fair share of security issues due to their continuous nature, involving frequent code changes, builds, tests, and deployments. Here are some common challenges: 1. : Nasty code can sneak into the pipeline and spread through deployments, opening the door to security problems. Code Injection 2. : Sometimes, sensitive data slips out during the CI/CD process, putting user privacy and compliance at risk. Data Leakage 3. : If permissions aren't set up right, it's an open invitation for unauthorized access and potential breaches. Access Control 4. : CI/CD leans on third-party bits, which can carry vulnerabilities that threaten the whole process. Dependency Vulnerabilities With these challenges in mind, taking proactive security steps is a must, and AIOps steps in as the solution. Introduction to AIOps in CI/CD Security AIOps, short for Artificial Intelligence for IT Operations, is an advanced approach that uses artificial intelligence and machine learning to improve various aspects of IT management. In CI/CD security, AIOps plays an important role by offering real-time monitoring, data analysis, predictive analytics, and automated responses to security threats. Here's how AIOps can strengthen security in your CI/CD pipelines. Automated Threat Detection A key aspect of AIOps is its capacity to automatically find security issues in CI/CD pipelines. By constantly monitoring pipeline actions, it can identify deviations from the norm. It checks code changes, the building process, and deployment phases for signs of wrongdoing. If someone attempts to slip in unauthorized code, AIOps promptly detects it and raises a security alert. Real-time Monitoring and Response Security in CI/CD needs constant watchfulness. AIOps is great at this, noticing odd things right when they happen. In a CI/CD setup, this means acting fast in security situations. For instance, if there's an unusual data transfer during deployment, AIOps can trigger alerts and take steps to stop it, preventing possible breaches. Predictive Security Analysis AIOps doesn't only respond at the moment; it's also proactive. Thanks to predictive analytics, it can foresee security problems before they happen. By studying past data and spotting patterns, it can predict possible threats and weak points in the pipeline. This helps security teams take steps ahead of time to keep things safe during the whole CI/CD process. Case Studies: Real-world Success with AIOps in CI/CD Security Now that we've covered the principles of AIOps in CI/CD security, it's essential to see how these principles are applied successfully in the real world. Several organizations have harnessed the power of AIOps to enhance their security in CI/CD pipelines. Here are a few notable companies: Google Amazon Microsoft Netflix Spotify Airbnb Uber Walmart Target Capital One JPMorgan Chase Bank of America These companies are tapping into the power of AIOps to automate security chores, speed up threat detection and fixes, and strengthen the security of their CI/CD pipelines. Take Google, for instance; they're using AIOps to sniff out and tackle security issues in their Kubernetes clusters. Amazon is also on board, using AIOps to automate security checks in their CI/CD pipelines. And Microsoft? They're making use of AIOps to dig into security logs and spot any fishy stuff going on. Implementation Strategies for AIOps in CI/CD Security When it comes to bringing AIOps into CI/CD security, it's a smart move that needs careful planning. Here are some important strategies to keep in mind: 1. : You've got to pick the AIOps tools that fit well with your CI/CD pipeline and security needs. Tool Selection 2. : Make sure that AIOps smoothly blends with your existing CI/CD tools and processes. Integration 3. : Figure out the right levels of monitoring and how you'll respond to security issues tailored to your specific pipeline. Monitoring Thresholds 4. : Train your security and operations teams so they can use AIOps tools effectively and act on responses. Staff Training 5. : Be sure to deal with concerns about data privacy and follow the rules in your industry. AIOps should always operate within legal and regulatory boundaries. Data Privacy and Compliance Benefits of AIOps in CI/CD Security Adopting AIOps into CI/CD security offers several advantages that can significantly impact your software development process: 1. : AIOps makes detecting threats almost instant, speeding up the process of spotting and dealing with potential security issues in your CI/CD pipeline. Swift Threat Detection 2. : Thanks to automation and real-time monitoring, AIOps allows for rapid responses to security incidents, keeping their impact to a minimum. Quick Response Times 3. : AIOps strengthens your overall CI/CD security with its automated threat detection and predictive analysis, lowering the chances of security breaches. Enhanced Overall Security 4. : AIOps can cut operational costs by automating many security tasks, freeing up your teams to focus on more strategic security efforts. Cost Savings 5. : AIOps systems can be set up to ensure compliance with industry standards and regulations, reducing the risk of non-compliance. Enhanced Compliance Challenges and Considerations While AIOps is a powerful ally in CI/CD security, there are some challenges and things to keep in mind: 1. AIOps deals with sensitive data, so safeguarding it and meeting privacy rules is crucial. Data Privacy: 2. : AIOps can sometimes trigger false alarms. It's important to tweak these systems to prevent your security team from being flooded with unnecessary alerts. False Alarms 3. : Setting up and managing AIOps systems requires a certain skill set. You might need training or skilled staff to handle it effectively. Skill Requirements 4. : Getting AIOps to work smoothly with your existing CI/CD tools can be tricky. Planning the integration carefully is a must. Integration Complexity Future Trends and Innovations The future of AIOps in CI/CD security looks bright, with some exciting trends and innovations on the horizon: - : AIOps will increasingly rely on understanding patterns and behavior to spot security threats. Behavior-Based Threat Detection - : AIOps will keep getting better at working smoothly with different CI/CD platforms, making it more adaptable. Cross-Platform Integration - : AIOps will become even smarter at predicting threats, making it more accurate. Enhanced Predictive Analytics , AIOps is like the secret weapon for CI/CD security. Its automated threat detection, real-time monitoring, and predictive analysis are key to keeping your CI/CD pipelines safe and sound. As software development keeps changing, AIOps will play a bigger role in securing the way your software gets delivered. In conclusion The future of CI/CD security depends on those who use AIOps to build secure, reliable, and efficient software delivery pipelines.

AIOps: The Secret Weapon for CI/CD Security

Dive into how AI is transforming the investment landscape Remember the early days of robo-advisors? They were like the first smartphones—revolutionary but limited. Now, generative AI is stepping in, offering more nuanced and personalized financial services. For instance, Q.ai offers customers investment kits picked by an AI that considers various data points, from market metrics to social media sentiment. 📊 Performance Metrics While AI's promise is high, its performance is still under scrutiny. ETF Managers Group's AIEQ, which uses IBM's Watson, has returned 4.9% over the last five years, trailing the S&P 500 index fund's 11.78%. It's like a new athlete who shows promise but has yet to win significant titles. 🛠️ Guardrails and Accountability AI isn't given free rein. Companies like Q.ai and AIEQ (see examples below) have human oversight to monitor AI output for biases and errors. Think of it as a self-driving car with a human driver ready to take over in complex situations. 🌐 Accessibility and Inclusion AI is democratizing financial advice. Firms like Fisdom in India use automated tools to offer financial guidance to a broader base of customers. It's like opening up an exclusive club to the general public, making high-quality advice accessible to all. 🤖 The Future: AI in the Back Office Even if AI isn't directly advising clients, it's finding a place in back-office systems. For example, it summarizes meeting transcripts, monitors risky transactions, and generates reports. Examples of startups / smaller companies using AI for investment management: 🤖 Q.ai Location: Jersey City, N.J. About: Q.ai offers no-fee "investment kits" consisting of 4 to 20 securities in a market sector. These kits are selected by an AI that evaluates market metrics, news, Google search trends, and social media sentiment. 📊 ETF Managers Group’s AIEQ Location: Summit, N.J. About: AIEQ uses IBM’s Watson AI platform to analyze millions of data points from various sources like news, social media, and financial statements of over 6,000 U.S. companies. 🌐 Nemo Location: Abu Dhabi About: Nemo uses a ChatGPT model to allow investors to ask questions they might typically ask a human broker. The company emphasizes that they don't let their AI go "off-leash." 📈 Toggle AI Location: Not specified About: Toggle AI aims to implement ChatGPT that will be programmatically constrained to provide reality-grounded answers to investors' questions. 🌟 Fisdom Location: India About: Fisdom uses automated tools to provide financial guidance to a broader base of customers, including individuals with low net-worth. 💼 Betterment Location: New York About: Betterment, one of the first mass-market robo-investing firms, remains cautious about using AI for financial advice. However, they do use AI in their back-office systems for tasks like summarizing meeting transcripts and diagnosing software issues. Examples of incumbent banks using AI for investment management: JPMorgan Chase: This financial giant is leveraging AI to optimize asset management and offer personalized investment solutions. Morgan Stanley is using AI to enhance its asset management strategies. UBS: Known for its wealth management services, UBS is adopting AI to improve its investment strategies and customer engagement. HSBC: This global bank is using AI to bolster compliance and risk management functions and augment and automate data analysis in the investment sector. 🔮 Summary The integration of generative AI into investment management is a significant development, offering more personalized and nuanced services. Performance metrics are crucial for assessing the efficacy of AI-driven investment platforms, and they currently show a mixed picture. The human oversight in AI-driven systems is a necessary safeguard, ensuring that the technology does not go "off-leash." Whether it's automating data analysis, enhancing customer engagement, or optimizing asset management, AI is playing a pivotal role in shaping the future of investment management. Excerpt from One More Thing in AI Newsletter. Get smarter on AI in 5 mins. Carefully curated from hundreds of sources for business leaders and founders. ) Join 1000+ subscribers for free.

How AI is Transforming the Investment Landscape

By Aoibhinn Mc Bride Holding your hands up and taking accountability for a mistake is never easy. So, spare a thought for the who are now admitting that hastily issuing return to office (RTO) orders wasn’t their best decision. 80% of business leaders A found that most leaders made their decision around forcing staff back to the office by analyzing keycard monitoring data and by seeing how many times meeting spaces were utilized, rather than by speaking to employees and taking their thoughts around returning to the office five days per week into consideration. new report by Envoy Additional data compiled by Owl Labs for its found that 69% of employees believe companies are insisting on a return to office because of traditional work expectations rather than functional benefits. State of Hybrid Work 2023 report Using employee monitoring data to ensure their staff are coming into the office is nothing new—companies including and have been using employee badge swipes to monitor in-office attendance for over a year—but is counting swipes the best way to make decisions around workplace location? JPMorgan Chase Google While 94% of those who took part in the Owl Labs survey shared that they could be convinced to come back to the office, 56% said their level of work-related stress increased since last year, highlighting that being in the office full-time isn’t serving everyone. Moving Forward So what can you do if your employer is insisting you wave goodbye to your home office for good and return to the office permanently? For starters, if commuting to an office every day is going to leave you worse off financially, you could ask for a pay rise to cover the cost—29% of those Owl Labs spoke to expect a pay increase if forced back to the office. Or perhaps you can speak to your manager about revising its technology so that it facilitates remote work more effectively, meaning you can adopt a hybrid working model instead. Additionally, you could try and negotiate a four-day week where you consolidate your work into four days but your . productivity and output stay the same However, if your employer is refusing to meet you half or even part of the way, one solution is to find a new job in a company that facilitates remote work. If so, the is the perfect place to start your search. HackerNoon Job Board It features thousands of jobs in companies that are actively hiring remote workers, including the three below. Senior Sales Engineer, Coder, Remote Coder is on a mission to build enterprise software that makes software developers’ lives easier and keeps them in flow. We are looking for a who will work with enterprise account executives to qualify, progress, and close revenue opportunities with Fortune 500 customers for Coder’s self-hosted Development Environment Platform. Senior Sales Engineer In this role, you will be a stakeholder in the technical relationship with customers, demonstrate the Coder platform from both the end user (developer) and administrator points of view, and install the Coder platform on various cloud and on-premise Kubernetes and VM infrastructure. . Find out more information here Product Manager, Search Platform Integration, Virtuoso Ltd, Remote Virtuoso is the leading global travel agency network specializing in luxury and experiential travel. The will oversee the integration of the Algolia search platform and play a crucial role in enhancing the search capabilities of the platform, ensuring a seamless and intuitive search experience for travel products, travel advisors, network-specific content, and travel inspiration content for valued travelers. Product Manager, Search Platform Integration As such, you will work closely with technical teams to define data structures, indexing strategies, and search algorithms that optimize search results across various content types. . See the full job description here Salesforce Solutions Architect, Vantage Point Consulting, Remote As a , you will play an integral role in the high satisfaction of Vantage Point's clients through the design and development of scalable, high-quality Salesforce services and solutions. Salesforce Solutions Architect The Solution Architect is responsible for owning the overall design of the technical application and recommending best practice solutions based on client business needs. . View additional details here Find your next job in tech today via the HackerNoon Job Board

Does Your Boss Regret Asking You to Come Back to the Office?

আপনার বস কি আপনাকে অফিসে ফিরে আসতে বলার জন্য অনুতপ্ত?

Bereut Ihr Chef es, Sie gebeten zu haben, ins Büro zurückzukehren?

¿Se arrepiente su jefe de haberle pedido que volviera a la oficina?

Votre patron regrette-t-il de vous avoir demandé de revenir au bureau ?

क्या आपके बॉस को आपको कार्यालय वापस आने के लिए कहने पर पछतावा है?

あなたの上司はあなたにオフィスに戻ってくるよう頼んだことを後悔していますか?

당신의 상사가 당신에게 사무실로 돌아오라고 한 것을 후회합니까?

Seu chefe se arrepende de ter pedido para você voltar ao escritório?

Сожалеет ли ваш начальник, что попросил вас вернуться в офис?

Patronunuz Ofise Geri Dönmenizi İstediğinden Pişman mı Oldu?

Sếp của bạn có hối hận khi mời bạn quay lại văn phòng không?

你的老板后悔让你回到办公室吗？

In the ever-changing modern business landscape, business growth strategies, once grounded in industry experience, competitive benchmarking, and a fair share of professional intuition, are being reshaped by the influence of . The sheer volume, variety, and velocity of data available to businesses today offer invaluable insights promising rapid growth and unprecedented efficiency. Big Data At the forefront of this revolution marches machine learning. Not just a passing trend, machine learning provides a rigorous mathematical framework to interpret patterns and make strategic decisions based on accurate predictions. But how exactly does machine learning modify and transform business growth strategies? And in what ways can its advanced algorithms help a company aiming for market leadership? In this article, I will try to answer these and other questions, exploring the connection between machine learning and efficient business development. Content Overview How Machine Learning Affects Modern Business What Are the Basic Algorithms of Machine Learning How Machine Learning is Applied to Business Growth in Practice A Step-by-step Guideline to Implementing Machine Learning for Business Growth Technology Limitations and Practical Considerations Conclusion How Machine Learning Affects Modern Business In today's business environment, data plays a central role. Every customer interaction and transaction generates data that offers insights into market dynamics. The core advantage of machine learning in business is its ability to transform this extensive data into actionable decisions. is a computational method that allows its practitioners to identify patterns and make data-driven decisions without explicit programming. In business contexts, it enhances decision-making by eliminating human biases. Instead of relying on lengthy manual analysis or basic market surveys, machine learning provides an in-depth, objective view by examining numerous variables simultaneously. Machine learning Take product pricing as an example. Instead of using conventional methods like cost-plus pricing, machine learning offers dynamic pricing models. These models factor in real-time demand, inventory, competitor prices, and even external events to recommend optional pricing. Similarly, in customer relations, machine learning refines the broad understanding of target audiences. By evaluating browsing patterns, purchase histories, and customer feedback, it assists in building personalized marketing strategies, boosting conversion rates and customer loyalty. This series of examples can be continued . ad infinitum The benefits machine learning brings to present-day business are innumerable. Today, in the third decade of the 21st century, the question is not whether companies should adopt machine learning, but rather how swiftly they can integrate it into their internal processes to avoid falling behind the competition. But first, let us ask ourselves a question, what do people mean by machine learning? What Are the Basic Algorithms of Machine Learning Machine learning leverages an intricate combination of mathematics and computation, yet it is much more than just a set of convenient tools for processing large volumes of data. Machine learning is a discipline rooted in established mathematical principles, which ensures the accuracy and reliability of the results it delivers. To give you a peek into the nature of this technology, I will provide a brief description of some basic machine learning algorithms. While this section would tell nothing new to experienced professionals utilizing the power of machine learning in their jobs, like data analysts, I hope that a reader new to the topic will find it informative. a way to predict one value based on another by discerning patterns from available data. Essentially, it determines the relationships between different variables, influencing one another. Linear regression, for instance, assumes a straight-line relationship between two variables, while polynomial regression describes curved relationships; yet another example is logistic regression which predicts the probability of a binary outcome (true or false). By understanding these relationships, businesses can predict the effect of a change in one variable on another. For example, Airbnb uses regression analysis to suggest optimal pricing for hosts based on factors such as location, property type, time of year, and other listings’ prices. Similarly, Zillow's "Zestimate" applies linear regression to predict house prices based on features like size, location, and number of rooms, leveraging vast amounts of past sales data. Regression Analysis provides play a pivotal role when dealing with massive volumes of unlabeled data. They segment data into distinct groups, with items put in groups based on inherent similarities – and differences from other groups. K-means, for example, partitions data into 'k' number of centroids, and all data points are clustered around these centroids based on similarity. The New York Times, as an example, utilizes clustering to group news articles for readers, ensuring that similar stories, whether thematic or topical, can be navigated with ease. Clustering algorithms involve analyzing available data and learning patterns to apply to new, unknown data. Often used in conjunction with clustering, classification is about assigning predefined labels to data points. One well-known application is in the finance sector. Banks like JPMorgan Chase & Co. leverage classification to discern between 'fraudulent' and 'legitimate' client transactions, drawing upon patterns in historical data. In the healthcare domain, IBM's Watson Health utilizes classification algorithms to assess medical imaging, helping doctors determine whether a tumor is benign or malignant based on specific attributes. Classification methods l: extracting meaningful, actionable patterns from data. By laying out a mathematical framework, machine learning ensures these patterns are not coincidences but have statistically significant foundations, providing reliable insights that businesses can use. This leads us to the next part of this article – a deep dive into the real-life applications of machine learning in business development. All these algorithms share a common goa How Machine Learning is Applied to Business Growth in Practice Machine learning’s theoretical foundation is captivating, but where its transformative power shines is in its ability to spark business growth through practical applications. To better understand its impact, let us dive into how it has reshaped various sectors in recent years. Nowadays, machine learning plays a key role in shaping . By analyzing vast data sets ranging from local consumer behavior, cultural preferences, and competitive landscapes to regulatory requirements, machine learning models predict the success rate of a product or service in a particular region or demographic. Such data-driven insights can guide businesses on where to open new stores, how to adjust product features for local tastes, or even when to launch for optimal impact. This predictive capability empowers businesses to make informed, strategic decisions, optimizing investments and amplifying their chances for successful market expansion. Market Expansion Strategies In the realm of , gone are the days of relying solely on traditional demographic-based methods. Clustering algorithms have revolutionized this space, enabling businesses to segment their clientele based on buying behavior, browsing patterns, and even sentiments on social media. This radical shift has paved the way for marketing campaigns that more precisely resonate with individual consumers, ultimately fostering stronger customer acquisition and retention rates. Customer Segmentation The complexity of – predicting future sales trends – has been an age-old challenge. But with machine learning, tools like regression and time series forecasting have introduced a new layer of precision. By intricately analyzing both historical data and external variables such as economic indicators or seasonal patterns, businesses now possess sophisticated tools to project revenue, enhancing their strategic decision-making process. Sales Forecasting In the dynamic world of commerce, setting the right price can make or break a product's success. Machine learning aids businesses in devising smart strategies by analyzing a combination of internal data (like production costs and desired margins) and external factors (like market demand, competitor pricing, and seasonal influences). These algorithms can dynamically adjust prices in real-time, promptly responding to changes in the market environment. By ensuring that products are priced competitively and profitably, businesses can boost sales volumes, maximize revenues, and gain a strategic edge in crowded markets. Pricing Optimization One more vital area where machine learning has showcased its value is in . By analyzing historical workflow data, machine learning can identify bottlenecks and inefficiencies holding back the business growth, or repetitive tasks ripe for automation. For instance, in manufacturing, predictive maintenance powered by machine learning can forecast when a piece of machinery is likely to fail, allowing for proactive maintenance, reducing downtime, and enhancing overall production efficiency. Operational Efficiency And last but not least, has also witnessed a profound transformation. Instead of grappling with the perennial challenges of overstocking and stockouts, businesses now harness machine learning's predictive analytics to predict future demand. This means more balanced stock levels, optimized supply chain operations, and the mitigation of lost sales opportunities. Inventory Management While these examples highlight just a few of the many ways machine learning has optimized business operations and business development, it is paramount to remember that the effectiveness of these models relies on data quality and the right algorithmic choices. A careful and informed approach to integration is necessary when deploying machine learning techniques and algorithms to develop new and facilitate existing business growth strategies. With my extensive experience in this area – harnessing the power of machine learning to benefit businesses, I can provide some practical insights on the matter. A Step-by-step Guideline to Implementing Machine Learning for Business Growth Integrating machine learning into business processes extends beyond merely deploying algorithms. It demands structured planning, teamwork, and continuous adjustment. Here are the key steps businesses should consider when embracing the power machine learning provides in their business development strategies: Before implementing data analysis or choosing a model, a company has to specify the intended objectives. Whether the aim is to expand to new markets, increase customer lifetime value, or optimize ongoing operations for better profitability, setting precise, quantifiable growth targets ensures machine learning projects stay on track with the set business goals. Establish clear business growth objectives Incorporating machine learning for growth is more than a data scientist's task. Effective integration often calls for insights from market strategists, sales experts, customer relationship managers, and tech specialists. By fostering a collaborative environment, businesses can ensure the resulting models are both technically robust and tailored to drive business expansion. Form a cross-functional team with a focus on growth Machine learning offers a variety of algorithms and tools, applicable to different growth challenges. For example, clustering algorithms can identify new market segments, while time series forecasting might predict sales trends. The choice of tools is dictated by the specific growth strategies. Select appropriate tools and algorithms After a model is created, the next step is to assess its performance with real-world data. Initial outcomes might not always hit the mark, but machine learning thrives on adjustment. Feedback and results guide the necessary model revisions for enhanced precision, and it is crucial to measure outcomes based on business growth indicators in focus – whether it is market share expansion, customer acquisition rates, or an increase in revenue. Adhere to iterative testing and refinement keeping business metrics in mind With a refined model ready, you can move to the deployment stage, integrating the model into strategic execution points. This can entail embedding insights into sales dashboards, incorporating predictions into marketing campaigns, or using forecasts in supply chain adjustments. It is critical to ensure smooth integration with current business infrastructure, enabling actionable insights that contribute to growth. Deploy and integrate As market conditions, business strategies, and customer preferences keep evolving, machine learning models must adapt. Regular monitoring is essential to track performance, with updates accommodating new data or changing market dynamics. Maintain regular oversight and adjustments By following a structured approach and implementing machine learning with clear growth-centric goals, businesses can unlock untapped growth and innovation opportunities. Still, even machine learning has its limitations, and the incorporation of its techniques and methods into business operations presents certain challenges to take into account before you start. Technology Limitations and Practical Considerations As businesses rush to harness the capabilities of machine learning, it is crucial to approach its adoption with a balanced perspective. Although the concerns I address here are common when implementing machine learning algorithms in general, if anything, they hold even more true when incorporating machine learning with the explicit goal of driving business growth. Understanding these limitations is of momentous importance to realizing the full potential of machine learning in bolstering growth strategies. One of the main things to consider while employing machine learning in your business is . Machine learning models thrive on data, and if the input data is flawed or incomplete, the output will mirror these imperfections. Businesses have to invest time and resources in data cleansing, preprocessing, and validation to ensure the reliability of the insights derived. data integrity Another critical thing one should watch out for in data sets is . Machine learning bias means that an algorithm will produce flawed results due to erroneous (biased) assumptions in the data the algorithm is trained on. If historical data carries inherent biases, the algorithm will likely reflect them. For businesses, this can lead to skewed customer insights or even discriminatory practices, and so active measures must be taken to identify and rectify such biases in the data beforehand. potential biases In the pursuit of high accuracy, there is a temptation to build complex models that perform exceptionally well on training data but falter when exposed to new, unseen data. This phenomenon, known as , can lead businesses astray with overly optimistic predictions or recommendations. Regular validation and testing against independent datasets are vital to ensure the model's generalization capabilities. overfitting Some advanced machine learning models, especially deep neural networks, are often termed "black boxes" due to their complex architectures. While they might offer superior accuracy, their workings can pose severe . For businesses, this poses a challenge in understanding and explaining the rationale behind specific predictions or decisions, especially in regulated sectors where transparency is imperative. interpretability issues In essence, while machine learning is a formidable tool in the business arsenal, its implementation demands a judicious blend of technical expertise and domain knowledge. The promise of machine learning is not in its infallibility but in its capacity to elevate decision-making when applied with due consideration of its limitations. Conclusion As the dynamics of the business world continue to evolve, the importance of staying adaptive and forward-thinking is paramount. It is evident that every day, data plays an increasingly important role in shaping modern commerce. As I hope I was able to demonstrate in this article, decision-making in business, and especially in building balanced and effective business growth strategies, has been deeply and irreversibly transformed by the integration of machine learning, adding depth and precision to once purely intuition-driven processes. Today, embracing machine learning is more than just a strategic move – it is a necessity for businesses aiming for consistent growth in a digital age. Yet, naturally, integrating this advanced technology is not without its complexities. Constructing systems that not only gather but deeply analyze data to provide actionable insights is a task that requires both innovative thinking and unyielding persistence. And while machine learning offers businesses innumerable advantages, it is the combination of this technology with human expertise that creates a robust foundation for productive business strategies. This blend ensures that decisions are both data-informed and grounded in human judgment – something that no technology has been able to replace.  At least – for now.

Leveraging ML Algorithms for Business Growth Optimization

11 Real-World Applications of Machine Learning in Ecommerce 

11 aplicaciones del mundo real del aprendizaje automático en el comercio electrónico

11 applications réelles de l'apprentissage automatique dans le commerce électronique

ईकॉमर्स में मशीन लर्निंग के 11 वास्तविक-विश्व अनुप्रयोग

電子商取引における機械学習の 11 の実世界の応用

11 aplicações do mundo real de aprendizado de máquina no comércio eletrônico

11 ứng dụng thực tế của Machine Learning trong thương mại điện tử

机器学习在电子商务中的 11 个实际应用

Leading technology companies like , Google, and even , have shifted their investments and resources from metaverse projects to AI chatbots. Therefore, the feeling that mixed reality worlds have stalled and lost momentum has become very real. Microsoft Meta , and AI human-like chatbots, , have replaced the metaverse as the next big thing in tech. Once again, we hear tech media analysts saying: “ .” And while they might think they are on to something, the metaverse is already a reality. It is now experiencing a slow but steady adoption. Here’s why that’s a good thing. ChatGPT often controversial The metaverse won’t reach mass adoption Slow Is Smooth, and Smooth Is Fast Forrester’s recognizes that the metaverse reached a fever peak as tech companies raced to develop the new mixed reality world. The report also reveals that the metaverse conversation became saturated, and its development will experience a slowdown tied to global economic uncertainties. However, Forrester assures that 3D content and Web3 will still become the new normal in the next ten years, replacing the 2D digital world. Predictions 2023: Fortune Favors The Bold And Focused New technologies and products often reach a hype moment and later enter a plateau. It’s during the plateau phase—where the metaverse is now—when technologies mature, new use cases are developed, and infrastructure is built. In areas like hybrid work, metaverse tools are slowly being introduced for remote workers. For example, Microsoft has been adding components to Teams to enable 3D metaverse-style feature collaboration. Google, Slack, Webex, and Zoom are all expected to do the same in 2023. Mesh When users think about the metaverse, they naturally link it with big tech companies and platforms like Decentraland, Roblox, and Fortnite. But there is a lot more going on behind the scenes. A Flexible Definition of the Metaverse Other areas where we see metaverse technologies applied to solve real-world problems include healthcare, industrial sectors with digital twins, and branding. says that with a potential to generate $5 trillion in value by 2030, the metaverse is too big for companies to ignore. McKinsey The key to understanding the value of the metaverse is to keep its definition flexible. Different sectors and companies are embracing this flexibility to create different use cases, services, and products. JPMorgan Chase, , believes the metaverse is an opportunity worth $1 trillion every year. On the other hand, Lenovo, one of the world’s leading personal technology companies, recently released its largest portfolio of edge-IoT-cloud hardware powered by AI, targeting industries developing metaverse digital twin solutions. And Siemens, working with NVIDIA, began creating their industrial metaverse, which focuses on engineering solutions, digital twins, IoT, and real-time analytics. the first bank to open in Decentraland From finance to electric energy to manufacturing, metaverse worlds are speeding up factory floors, running security and maintenance, executing all types of simulations, streamlining supply chains, and increasing safety. Gartner’s says the metaverse has the potential to protect a brand and help it grow, attract and retain talent and customers, and increase revenue. The technological research and consulting firm says that by 2027, more than 40% of large global organizations will use some form of Web3 technology, including spatial computing and digital twins in metaverse-based projects. 2023 Top 10 Strategic Technology Trends Brands that recognize the business value of the metaverse and hybrid world experiences have also connected with global users as consumer trends evolve, demanding modern hyper-personalized experiences. “Consumer behavior has largely shifted toward adopting digital personas, yet many brands have yet to provide a solution,” Irene-Marie Seelig, co-founder and CEO of AnamXR, told . McKinsey Keeping the definition of the metaverse flexible is also understanding that the concept is already heavily in play. Matt Ball, McKinsey knowledge managing partner, adds: “There are hundreds of millions of people connecting to these environments every day. There are many of the most storied companies on earth building a presence, and we have commerce in the tens and soon to be hundreds of billions of dollars.” All these new metaverse features, services, and solutions will not only drive adoption. They will lay the foundations for the second generation of metaverse hardware and software, and start off workers, professionals, and global users on the metaverse learning curve. Engineering Quality and Safety Into the metaverse The chaotic, rollercoaster release of ChatGPT, and similar AI human-like chat models, not only triggered the between Microsoft, Google, and others like and rushing to lead the market. It also taught the world a lesson, if we are willing to pay attention: Big tech will release products even when they are not finished, thoroughly tested, verified, or working at 100%. AI wars You.com DuckDuckGo Unlike any other sector, in technology, companies can instantly release products and services globally, in many cases, without going through exhaustive safety and standard checks. In the real world, the way products are released can be very different. Users have tragically experienced what happens when companies—even those with excellent reputations—release poorly constructed or unsafe products. For example, the collapse of FTX, the crypto currency exchange. On November 11, FTX filed for bankruptcy protection leaving more than one million customers and an estimate of $8b in their funds missing. The founder of FTX, , aka SBF, was charged by the with conspiracy to commit wire fraud, wire fraud, conspiracy to commit commodities fraud, securities fraud, money laundering, and conspiracy to defraud the Federal Election Commission and commit campaign finance violations. Samuel Bankman-Fried U.S. government While many may argue that technology products do not require oversight, this is not always the case. We see Beta versions of all types of software rolling out; take Tesla’s self-driving software, allegedly responsible for several fatal crashes and . currently under investigation Undoubtedly, the world needs to have a profound debate about the release of tech products that have significant implications and risks for the population. Mainstream technology should only be released once it is fully functional and working as it should. For metaverse companies and organizations, this particular time can be a beneficial one. metaverse projects are no longer subject to the insane pace of the rushing competition that comes with being the “latest best thing in tech.” This set of unique circumstances allows developers to build infrastructure, test their products, experiment with new use cases, and make sure they develop safe products of quality. Metaverse solutions, whether social media virtual worlds, finance applications, or working with basic public services, must be resilient to cyberattacks and should never pose ethical or compliance risks. They must also positively benefit users and communities. This can only be accomplished when a company focuses on building high-quality, safe products and services, not when they are pressed to meet unrealistic deadlines under pressure from Wall Street indexes like NASDAQ. tech-heavy stock Accessibility, ethics, security, and compliance must not be an afterthought; they must be embedded into the vision and source code from the start of every metaverse project. Facilitated by AI advancements and powered by the edge and the cloud, developers have a wide range of resources available that they can use. Today, startups, as well as small, medium, and large companies, have a once-in-a-lifetime opportunity to take their time engineering the metaverse—and get it right. This article was originally published by Austin Yavorsky on . 150sec

Metaverse Adoption is Painfully Slow and That's a Good Thing

মেটাভার্স অ্যাডপশন বেদনাদায়ক ধীর এবং এটি একটি ভাল জিনিস

Die Einführung von Metaverse ist schmerzhaft langsam und das ist eine gute Sache

La adopción del metaverso es dolorosamente lenta y eso es algo bueno

L'adoption du métaverse est douloureusement lente et c'est une bonne chose

मेटावर्स दत्तक ग्रहण बहुत धीमा है और यह एक अच्छी बात है

メタバースの採用は痛々しいほど遅いが、それは良いことだ

메타버스 채택은 고통스러울 정도로 느리고 이는 좋은 일입니다.

A adoção do metaverso é dolorosamente lenta e isso é uma coisa boa

Metaevrenin Benimsenmesi Acı Verecek Kadar Yavaş ve Bu İyi Bir Şey

Việc áp dụng Metaverse rất chậm và đó là một điều tốt

Metaverse 的采用非常缓慢，但这是一件好事

Reliability and decentralization are among the most important advantages of cryptocurrencies and crypto assets. Such freedom of action provides an opportunity to reduce the cost of using crypto by increasing the speed of transfers. However, when can freedom turn into arbitrariness? The case of the FTX exchange and cost traders and investors millions of dollars, highlighting the significant weakness of the regulatory framework in the industry. its sudden bankruptcy Although experts and participants have long pointed out these imperfections, regulatory institutions have continued to delay decisions. Solutions should appear soon, as most of the world's attention is focused on the problem. However, certain questions remain relevant. How do we implement effective regulation of the crypto industry without harming its development? Increase in the Level of Transparency in Asset Use Transparency of financial reporting has long been an essential part of the modern business industry. The concept of "transparency" refers to the publication of quarterly and annual reports, including financial results with in-depth descriptions. At the same time, annual financial statements are being audited by independent companies. Auditors provide a conclusion on the correctness of the specified data and the quality of internal control over the company's financial reporting process. This approach significantly increases the level of trust in published data on the part of shareholders and investors. In addition, the process of review of reporting by government regulators is greatly facilitated through standardization. Are these methods appropriate for the crypto industry? Undoubtedly, the practice of publishing financial statements will significantly increase the level of transparency for cryptocurrency exchanges and platforms. How exactly can this process be implemented? The individuals can rely on the experience of stock exchanges working as public companies with shares placed on their platforms. The public status of stock exchanges implies publishing financial statements with auditors' conclusions in accordance with current legislation. Thus, such exchanges as and provide the highest level of transparency by choosing public status. NYSE NASDAQ An in-depth analysis shows that cryptocurrency exchanges lag behind stock exchanges regarding transparency. The case of the recent bankruptcy of FTX reveals significant gaps in financial reporting. It is necessary to say that FTX has had a mind-boggling in cash at the moment of bankruptcy. For example, neither FTX nor Alameda Research, affiliated with , had departments. Such gaps cause potentially fraudulent actions, bankruptcies, and financial losses for investors. $1.4 billion Sam Bankman-Fried financial reporting Strengthening Control Over the Largest Participants The choice of public status by stock exchanges was entirely voluntary. Certain crypto exchanges operate as private companies with one or more owners, even now. In addition, these platforms are registered in offshore zones, complicating their regulation process. The relatively young status of the industry, the desire to avoid additional costs, and the preservation of full control over management decisions led to the emergence of this situation. At the current stage of the industry's development, publicity has not yet been generally accepted by contradicting the norms of transparency. It requires a more advanced level of control. Public status involves the placement of shares on the stock exchange and an increase in the number of holders. The owners perceive their ability to influence the company's management as a potential threat. Furthermore, the preparation of public financial statements, as well as their audit, requires additional costs, providing mostly intangible benefits. The ability to recognize these advantages remains underestimated by owners of crypto exchanges. Many small platforms cannot afford getting public statuses with all related obligations. Despite the above caveats, the crypto industry needs regulation. The negative case of FTX and its owner showed the painful consequences of insufficient regulation. In addition to significant losses of investors’ funds, the bankruptcy of one of the industry leaders significantly undermined confidence in crypto exchanges, cryptocurrencies, and crypto assets. Such well-known corporations as . became victims of FTX’s bankruptcy as the exchange’s creditors. Mistrust has long-term problems, especially for industries and companies in the development stage. Goldman Sachs Group Inc., JPMorgan Chase & Co. andWells Fargo & Co It is important to emphasize that the crypto industry is not unique regarding major crises. One can recall the bankruptcy of the company and the termination of Arthur Andersen whih was among the top five auditing companies in 2001. Likewise, the bankruptcy of one of the largest US banks, , in 2008 catalyzed the global economic crisis. Both bankruptcies led to the introduction of new regulatory instruments, namely the Sarbanes-Oxley Act in 2002 and the Dodd-Frank Act in 2010, respectively. Enron Lehman Brothers Therefore, it is reasonable to expect that the bankruptcy of FTX will lead to increased regulatory control. The has had irreversible consequences for the industry in 2023. The recent bankruptcy of is one more confirmation of the need for urgent changes. “crypto winter” Celsius Implementation of Requirements for Crypto Exchanges Even at this stage, it is not clear what the regulatory framework for crypto exchanges will look like. Individual approaches to the issue in different countries complicate the process of control at the global level. The company's registration in the Bahamas, with its limited regulatory control and ability to oversee financial services companies, was one of the reasons for FTX's bankruptcy. This situation made it possible for the owner of FTX to conduct risky operations with signs of fraud. Strengthening regulatory requirements for crypto exchanges is set to become a real solution to the problem. The first step should imply the creation of a registry of licenses recognized in all countries of the world, allowing transactions with cryptocurrencies. Thus, crypto exchanges can obtain licenses like the Malta VFA Class 4 License. Such a license confirms the sufficient level of security of the exchange for its clients. At the same time, it is necessary to limit the possibility of using exchange clients' funds in high-risk transactions. Control of such a restriction can take place in the form of periodic inspections of the stock exchanges for the use of funds. These steps will help restore and increase the level of trust in crypto exchanges and prevent large-scale bankruptcies, as happened in the case of FTX.

JPMorgan Chase

jpmorganchase.com

256710 employees

Since 2000

COMPANY RANKING

JPMorgan Chase

EVERGREEN INDEX #2367

See more notable links about JPMorgan Chase

Apply to get your Tech Company News Page live on HackerNoon.com today!

Read More Tech Stories Related to #JPMorgan Chase

JPMorgan Chase

JPMorgan Chase

jpmorganchase.com

256710 employees

Since 2000

COMPANY RANKING

JPMorgan Chase

EVERGREEN INDEX #2367

Related CompaniesRank

JPMorgan Chase Story MentionsComplete List

See more notable links about JPMorgan Chase

Apply to get your Tech Company News Page live on HackerNoon.com today!

Read More Tech Stories Related to #JPMorgan Chase

JPMorgan Chase