Hackernoon logoThe Awesome Duo: 6 Cases of How FinTech Benefits From AI by@viceasytiger

The Awesome Duo: 6 Cases of How FinTech Benefits From AI

The Awesome Duo: 6 Cases of How FinTech Benefits From AI: 6 cases of effective ML, NLP and deep learning use in the fintech industry. In 2018, about 61% of Americans used digital banking services and this number is set to exceed 65% in 2022. Companies that use robotic process automation (RPA) typically see 40%-100% ROI within only 3–8 months of implementation. In the U.S. alone, AI will help banks and financial institutions save more than $1 trillion.
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Vik Bogdanov

Tech storyteller

Photo by Alice Pasqual on Unsplash

If you’ve ever used the Internet to transfer money between accounts or apply for a bank loan or trade, you’re probably aware of how deeply rooted fintech has become in our day-to-day lives. In 2018, about 61% of Americans used digital banking services and this number is set to exceed 65% in 2022. One of the newly-emerged traits of the 4th Industrial Era, fintech is an application of fast-evolving digital technologies to improve and facilitate financial services.

Companies are rapidly adopting fintech to keep abreast of the competition. The investments into this industry are also impressive: in 2018, it attracted over $16 billion investment in the UK alone, according to KMPG.

On the other hand, entire countries are rapidly adopting AI technologies to compete for the biggest piece of the pie.

South Korea released an ambitious national plan to invest $2 billion to strengthen its AI R&D capability by 2022. The plan includes the establishment of 6 new AI Research Institutes across the country. The country ranks 1st globally for R&D spending as a percentage of GDP (4.3%); in particular, a huge part of that spending covers AI.

One more country that’s aiming to reserve its seat on the bandwagon with the world’s leading AI-driven nations is Israel. In 2018, AI startups raised over $1.5 billion in funds.

Israel is a hotbed for AI talent, tracking at almost 4,000 developers, engineers and data scientists working on AI R&D and integration (software and hardware included). Of them, 64% are employed by startups, while 31% work for both local and multinational corporations with dedicated AI centers/labs based in Israel.

However, the demand for AI solutions development, deployment, and maintenance is so high all over the world that the current national pools are simply not sufficient enough to meet the demand. It makes many FinTech companies leverage external AI talent pools to avoid being “locked” in domestic talent deficiencies and in order to speed up their time to market.

For instance, an Israel-based 3rd generation blockchain platform SkyCoin couldn’t find enough Golang engineers for its AI-based solution development and hired a dedicated software team in Ukraine to accelerate time to market and access a much larger AI talent pool than the one available in their home country.

FinTech is often believed to be the catalyst of the global AI-driven change, as many banks and financial organizations have already had a chance to pioneer AI to a certain extent and reap benefits, while many other industries stayed apart and waited to see the first successful use cases and justifiable ROI.

According to FinTech Symposium 2018, companies that use robotic process automation (RPA) typically see 40%-100% ROI within only 3–8 months of implementation.

The predictions are even more sensational. As per Autonomous Research, a U.S.-based independent analytical agency specialized in FinTech, in the United States alone, AI will help banks and financial institutions save more than $1 trillion (as a result of RPA and other AI technologies); $490 billion is expected to be saved with a substantial reduction in the number of specialists in cash operations, security personnel, and other staff.

As you can see, AI and nascent technologies will be playing a key role in fintech transformation into a mature industry in 2019 and beyond.

Let’s take a look at 6 cases of effective ML, NLP and deep learning use in the fintech industry to have a better understanding of how powerful and unique the transformational nature of AI is.

1) Customer service bots

According to the number of studies conducted in recent years, the overwhelming majority of US millennials would happily ditch their banks, if presented with a viable alternative. This tech-savvy generation had listed difficulty resolving problems, standing in long lines and unpleasant interaction with bank personnel among things they hate most about their banking experience.

The use of natural language processing (NLP), a set of technologies aimed at recognizing human language and speech, has propelled the evolution of chatbots to the level where they can perform an impressive range of operations: from virtual assistants to automatic claims processing. Virtual assistants turn the previously daunting experience into a pleasant one. NLP-based chatbots help customers sort through a plethora of financial products, create savings plans and control their spendings. For example, Ella, a digital coach created by Sun Life, helps customers navigate through their benefits and pension plans.

In insuretech, most of the operations facilitated by AI use are customer-related: personalized telematic devices that track driving and fitness trackers reporting to insurance companies on exercise and health play an important part in client risk profiling used by insurance companies. AI tools then automatically select insurance products suitable for each risk profile and offer them to customers via virtual advisors.

2) Creditworthiness assessment

NLP is the driver behind a so-called financial inclusion, helping maximize the accessibility of banking services for the previously unbanked population. For example, while in the USA and EU banks can assess customers credit history to evaluate creditworthiness, in developing countries most customers don’t have any credit history at all. Here’s when NLP and advanced text mining come into play: by analyzing the digital footprint customers leave while browsing the internet and using social media, the software generates a credit score that helps precisely predict their further behavior.

3) Algorithmic trading

In 2017, according to Techfunnel, as much as 73% of daily trading activity was carried out by ML algorithms. Today, financial companies increasingly acknowledge the benefits of algorithmic trading: it goes along predefined rules, reduces slippages, requires no time-consuming market monitoring and, most importantly, it’s free of human emotions, which are often to blame for high fallacy rates. As fintech evolves, more and more organizations are willing to trust machine learning rather than human intuition.

4) Predictive analytics

Another vivid example of ML use in fintech is predictive analytics. By promptly capturing, processing and analyzing massive data sets, businesses are capable of making faster and more precise predictions of future financial trends than with traditional methods. As of today, traditional analytic tools are being steadily replaced by machine learning algorithms which help analyze data, predict risks and identify opportunities.

5) Natural language search

In customer service, NLP is applied to help clients search for transactions with a particular company or service, but natural language search is also a highly useful feature for sifting through internal company data. Financial companies need instant on-demand access to internal data to keep up with the competition. Natural language search enables organizations to perform such a search in a matter of seconds. NLP translates a human language into a SQL request and has results delivered in convenient visual form. In modern markets, when banks operate 24/7 in different time zones having relevant information at hand can save billions of dollars and help make well informed strategic decisions.

6) Fraud detection

Technological advancements often result in more fraud risks and security breaches for financial institutions. In fact, no other industry suffers from fraud-related losses to the extent financial industry does. In 2018, according to Javelin’s Identity Fraud Report, about 17 million organizations in the US have experienced fraud. Fortunately, ML fraud detection tools are also getting more advanced and have proved to be far more effective than traditional manual methods. ML algorithms detect anomalies in the real-time, use lower numbers of verification measures, and identify hidden fraudulent activities. Banking transactions and biometric user authentication are just some of the examples of ML use in detecting fraud.

Regtech is another newly emerged fintech segment. In plain language, it is defined as “using new technology to facilitate the delivery of regulatory requirements ”, which are becoming ever more complex and omnipresent in modern fast-developing markets. For example, companies like Comply Advantage leverage AI and machine learning to build apps that detect money laundering and terrorist financing and help companies comply with global regulations and protect their business.”

Driven by the advent of AI, increased use of mobile devices and the Internet, fintech market has been reported to have the highest expected CAGR of 74.16% during the 2014–2025 forecast period. Initially used to power backend processes of most financial institutions, fintech is now the main force behind customer-related financial operations and is transforming the way we handle finances on the global scale. Admittedly, what’s hindering its adoption is lack of qualified AI consultants, but, in the long run, companies can tackle this issue by leveraging AI outsourcing and partnering with reliable third-party providers to advance their financial services.

And how else does AI disrupt fintech these days?


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