Blog article
See all stories »

Is AI The Right Fuel For Fintech?

The banking and financial industry have always been early adopters of the latest tech gadgets, in the hope that these investments would grant them a competitive advantage, a cost reduction or improved processes.

AI is the latest and most promising technology so far since it aims to mimic and enhance human abilities. It can perform fast computations, pattern detection and replicate understanding of spoken language. It is expected to replace or at least help employees on different levels, ranging from call center agents to portfolio managers and stock brokers.

There is positive hype around this technology and venture capitalists are not shy to bet on Fintech as an attractive investment. KPMG notes that this rose to over $31 billion in 2017, which was increase of 34% compared to 2016, so there is a clear attractiveness.

Why Use AI for Fintech?

AI’s goal is to mimic the functionality of the human brain. It does this by learning about a topic through extensive exposure and then identifying similar occurrences in new samples and classifying them accordingly. For example. It could look at past exchange rates, interest rates or investment portfolios and identify those patterns that lead to higher returns for the client. This pattern analysis feature could be useful for security purposes and to prevent frauds.

Another use of AI is to replicate human communication functions. By NLP (natural language processing) the machine can act as a call center agent or as a personal account manager. It can help clients find answers to their questions, give them advice or guide them through product set-up. Once trained, it’s much cheaper and reliable compared to its human counterparts.

A third reason to use AI is that it offers a great way to store and pass on accumulated knowledge. For example, a pre-trained network can be used to serve a similar purpose with minimal intervention. This creates the opportunity for a modular approach and decreasing the time to market.

AI’s Applications in Fintech

Employing AI in fintech is just in its infancy, yet there are already numerous applications or ideas around. Let’s go through the most feasible and discuss the benefits and challenges.


The banking and finance industry used to rely on a personal approach, especially for private clients. Personal bankers and tellers can now be replaced by AI-powered chatbots. This customer-facing role of technology is a massive bet for fintech companies since it can enhance or destroy client relationships. If the bot is well-designed and offers flawless service, the clients, especially younger ones will feel that the company cares about their needs and preferences. On the other hand, a clumsy bot which goes around in circles without offering relevant solutions and no connection to a human operator can be a good enough reason to switch providers.

A particular type of chatbots are robo-advisors.  Some act as personal portfolio managers and by asking the client a few questions they create a profile based on investment goals, risk appetite, and other information like preferred industries. They allocate assets, generate balance projections and suggest portfolio changes.

Other robo-advisors are created by loan and mortgage companies to help their clients stay on track with payments and manage their budget diligently. Clients can design an ideal budget allocation, and the AI will dynamically adjust spending to help them say within the set limits on a daily, weekly or monthly basis.

Underwriting and Insurance

Risk hedging in the insurance business is based on creating risk assessment models from historical data. An AI system can do the same by scanning through thousands of past insurance records and evaluating those recurring factors which tend to have a real influence on accidents. In fact, by using AI, new evaluation models could emerge. These would be based on proven causality instead of statistically possible influences. Automating the underwriting process can offer improvements both in term of speed and accuracy.

Data Processing for Insights

The real power of AI is not only in immediate consumer-level applications but in long-term assessment of market trends. In a similar manner to the use of the technology for insurance, AI can analyze loan applications and look well beyond the FICO score.

Algorithms look at the customers’ past behaviors and can make accurate predictions about future choices. AI developers claim that this method is supposed to be more accurate than the general scoring methods used so far due to pattern analysis without previous input. Basically, the neural network does not know beforehand what to look for, it just searches for meaningful connections.

Such tools are also great for management purposes since the machine will never take emotionally biased decisions, unlike humans. The risk is that the computer can classify limit cases into a different category. Yet, this is worth considering for the potentially innovative insights that the method can uncover.

Automation Opportunities

The most significant fear of people working in banking and finance in lower or middle levels right now is related to AI’s ability to automate their jobs to the point of eliminating them. This is a standard evolution of IT, and a similar process took place when work stations were introduced in the 80s or with the rise of the Internet in the 90s.

Automation is a definite goal which can improve the bottom line for larger companies due to essential savings generated by volume. Also, it can make a difference for smaller companies as it can replace entire departments like a marketing team or enhance the work of an HR professional. Through affordable AI, a start-up potentially has the same tools as an established company and a decreased chance of failing in the first few years due to lack of skilled talent. 


Currently, Fintech is like the wild west—competitive and with few rules. It’s a place of promises, unicorn start-ups, and bitter disappointments and underdelivering. Those who are able to harness the power of AI cost-effectively have a good chance of improving not only their bottom line but their chances of surviving in the next decade.

External | what does this mean?
This content is provided by an external author without editing by Finextra. It expresses the views and opinions of the author.

Comments: (0)