The 4 main uses for AI in FinTech | 2019

24-04-2019 | Industry news

How is machine learning used in FinTech? 

Machine Learning and Artificial Intelligence are both terms that have seen recent exponential growth in use by companies. With that being said, these buzzwords are not only being used because they sound particularly interesting but because AI has become a significant facet in the future of FinTech. Financial institutions around the world are trying to adopt & implement AI into their service capabilities. As the FinTech industry has a widespread reach, the applications for AI/machine learning technologies exist throughout the entire range of these business services.

In this blog, we present 4 uses for AI in FinTech that have proven to be actionable and practical for use in or by Financial Institutions in 2019.

Customer Support

Customer Experience is key in the current competitive environment. With that being said, modern technologies are allowing financial institutions to take their services online. AI tools are providing customers information that once required real human time, now the computer is able to give in-depth information.

There are two evident uses for AI in customer support, the first one, and the most likely for consumers to encounter is the chatbot. AI supported Chatbots are used by financial institutions to provide customers with information about their balances, transactional data, and other related matters instantly.  

Kasisto – AI Financial Chatbot | Fintech Awards Winner 2018

Secondly, the personalization of the services provided through machine learning tools and the customer data analysis is of a higher quality. This allows the customer support to anticipate the needs of the customer through the predictions or advice of the ML tools.

Trading and Asset Management

One of the most prominent shifts from traditional technologies to machine learning has been in trading and investments. Whilst still on the brink of mass adoption by the general public, machine learning algorithms for predicting fund trends is being widely used by financial companies. Generally speaking, FI’s use AI to automate their financial decisions and improve the overall trade amounts & frequency.

With 73% of the daily trading being performed by machines (YTD) in 2017, vast amounts of financial institutions are investing their resources into their own algorithmic trading tools.

Through machine learning algorithms, fund managers are able to spot market changes earlier than traditional investment models. Additionally, algorithmic trading is free of emotions, there’s no need to monitor the market and losses through forex trading slippage reduce significantly.

Algorithmic trading is not a recent groundbreaking development, it has been in play for a substantial amount of time. The implications of Artificial Intelligence, however, provide traders with technology that does not only automate trading but also carries out “intelligent actions”

The AI can break down market behavior from the past, create trading strategies, make predictions, and more.

Compliance & Fraud Detection

The current FinTech industry is one where the ongoing technological innovations require financial institutions to protect themselves from the risks of fraudulent transactions. Data shows that financial institutions are 300% more likely to fall victim of cyber security attacks compared to other industries. This is why companies are using AI to detect fraudulent and abnormal financial behavior, and/or using AI to improve general regulatory compliance matters and workflows.

In the financial industry, and therefore the FinTech industry, there are two approaches to fraud detection. These are Rules-based approach and Machine Learning-based approach.

Fraud prevention and detection approaches | RubyGarage

With fraud being such a threat, financial companies are shifting from the Rules-based approach to the ML-based approach. The AI tools allow FI’s to identify and prevent fraudulent transactions through their high-volume analysis of data. Detection rates are lower, there are fewer false positives and the tools result in easier regulatory compliance.

Commerzbank, for example, is planning to use Machine Learning to automate 80% of its compliance checklist processes by 2020. Commerzbank is using the technology of Amsterdam-based Conpend to automate pre-compliance checks for traditionally paper-based trade finance transactions. Initially, the process is tasked with detecting money laundering with AI tools.

Credit Scoring / Direct Lending

Nowadays, the value of the majority of the loans is derived from data which calculates the probability that an individual or business will pay the loan back. Determining the likelihood of someone defaulting is critical, but has proven to be a tough task. This mainly is due to the fact that the data that the assessor has to work with is inaccurate, or even untruthful. Financial Institutions, therefore, are using AI for risk assessments.

Furthermore, lenders are now able to asses one’s entire digital footprint and determine their likeliness to default. Lenders used to only look at the few metrics that were available such as income, and debt. Nowadays, alongside the standard data, lenders are also able to have a look at “alternative data” about potential borrowers. For now,  however, the “alternative data” analysis is not aimed at getting more insights into potential lender’s personal financial data. The goal is to determine the creditworthiness of those who do not have a (traditional) credit history.

Conclusion

It’s hard to deny the importance of AI and Machine Learning in the future of finance, but is there already too much emphasis being placed on the immediate necessity? How do we strike the balance between staying true to tried and trusted ‘traditional’ methods, while maintaining agility and speed of development? Only the future can tell how influential AI & Machine Learning will be…

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