4 ways Artificial Intelligence is leading disruption in Fintech

Widely used AI-powered applications such as Virtual Assistants, Chatbots, Algorithmic Trading and Purchase Recommendation systems are fueling up the businesses with low marginal costs, growing revenues and providing a better customer experience. According to a survey, by National Business Research Institute, more than 62% of the companies will deploy AI powered fintech solutions in their applications to identify new opportunities and areas to scale the business higher.

What has led the disruption?

The Financial sector is experiencing a faster technological evolution right from providing personalized financial services, executing smart operations to simplify the complex and repetitive process. Use of machine learning and predictive analytics has enabled financial companies to provide smart suggestions on buying and selling stocks, bonds and commodities. Insurance companies are accelerating in automating their loan applications, thereby saving umpteen number of hours.

Leading Investment Bank, Goldman Sachs automated their stock trading business replacing their trading professionals with computer engineers. Black Rock, one of the world’s largest asset management company facilitates high net worth investors with automated advice platform superseding highly paid wall street professionals. Applications such as algorithmic trading, personal chatbots, fraud prevention & detection, stock recommendations, and credit risk assessment are the ones finding their merit in banking and financial services companies.  

Let us understand the changing scenarios with next-gen technologies:

Fraud Prevention & Detection

Fraud prevention is tackled by the firms using an anomaly detection API. The API is designed using machine learning & deep learning mechanism. It helps identify and report any suspicious or fraudulent activity taking place among-st billions of transactions on a daily basis. Fintech companies are infusing huge capital to handle cyber-crime, resulting into a global market spends of more than 400 billion dollars annually. Multi-national giants such as MasterCard, Sun Financial, Goldman Sachs, and Bank of England use AI-powered systems to safeguard and prevent money laundering, banking frauds and illegal transactions. Danske Bank, a renowned Nordic-based financial service provider, deployed AI engines in their operations helping them investigate millions of online banking transactions in less than a second. With this, cost of fraud investigation and delivering faster actionable insights reduced drastically.

AI Powered Chatbots

Chatbots are automated customer support chat applications powered by Natural Language Processing (NLP). They help deliver quick, engaging, personalized, and effective conversation to the end user. With an upsurge in the number of investors and varied investment options, customers seek financial guidance, profitable investment options and query resolution, faster and in real-time. Large number of banks such as Barclays, Bank of America, JPMorgan Chase are widely using AI-supported digital Chatbots to automate their client support, delivering effective customer experience with smarter financial decisions.

Bank of America, the largest bank in US launched Erica, a Chatbot which guides customers with investment option notification, easy bill payments, and weekly update on their mortgage score.  MasterCard offers a chatbot to their customers which not only allows them to review their bank balance or transaction history but also facilitates seamless payments worldwide.

Credit Risk Management

For money lenders, the most common business risk is the credit risk and that piles up largely due to inaccurate credit risk assessment of borrowers. If you are unaware of the term credit risk, it is simply a risk associated with a borrower defaulting to repay the loan amount. AI backed Credit Risk evaluation tools developed using predictive analytics and advanced machine learning techniques has enabled bankers and financial service providers to simplify the borrower’s credit evaluation thereby transforming the labor intensive scorecard assessment method.

Wells Fargo, an American international banking company adopted AI technology in executing mortgage verification and loan processing. It resulted in lower market exposure risk of their lending assets. With this, the team was able to establish smarter and faster credit risk management functionality. It resulted in analysis of millions of structured and unstructured data points for investigation thereby proving AI as an extremely valuable asset for credit security and assessment.

Algorithmic Trading

More than half a dozen US citizens own individual stocks, mutual funds, and exchange-traded mutual funds. Also, a good number of users trade on a daily basis, making it imperative for major broking and financial trading companies to offer AI powered algorithmic trading platform. The platform enables customers with strategic execution of trades offering significant returns. The algorithms analyse hundreds of millions of data pointers and draw down a decisive trading pattern enabling traders to book higher profits every microsecond of the trading hour.

France-based international bank BNP Paribas deployed algorithmic trading which aids their customers in executing trades strategically and provides graphical representation of stock market liquidity. With the help of this, customers are able to determine the most appropriate ways of executing trade under various market conditions. The advances in automated trading has assisted users with suggestions and rich insights, helping humans to take better decisions.

How do we see the Future of AI in Financial sector?

The influence of AI in fintech has marked disruption in almost each and every financial institution, right from investment banks to retail banking, to small credit unions. Data science and machine learning practitioners are endeavoring to position AI as an essential part of the banking ecosystem. Financial companies are synergizing with data analytics and fintech professionals to orient AI as the primary interface for interaction with their customers.

However, the sector commonly faces challenges in adoption of emerging technologies, making it inevitable for AI too. The foremost challenge companies face is availability of massive data which is clean and rich to train machine learning algorithms. The next hurdle in line would be the reliability and accuracy of the data insights provided by the AI mechanized solution. With dynamic market situation, businesses could experience decline in efficacy of their models causing serious harm to the company. Hence, they need to be smarter and cannot solely trust the AI technology in achieving the business mission.

Absence of emotional intelligence in Chatbots is another area of concern resulting in an unsatisfactory customer service experience. While there may be other roadblocks, the rising investment in AI technology would definitely assist financial companies in overcoming such challenges and developing competitive intelligence in their product offerings.

Predicting the near future, adoption of cutting edge technologies such as machine learning and predictive analytics will boost higher customer engagement, exceptional banking experience, lesser frauds and higher operating margins for banks, financial institutions and Insurance companies.