Bring on Your AI Game to Finance Services

The technology revolution has been shifting the landscape in financial sectors – with the rise of Fintechs, Blockchains and Cryptocurrencies threatening the existing models of the traditional institutions. Many traditional institutions realized the importance of, and have taken steps to adopt big data strategies. Being in a competitive space and also one of highest data generating industry with big guns, CEOs and other top managements are eager to embark on technology growth strategies. Over 80% of financial service firms expressed that they are planning (26%), trialing (36%) or have fully deployed (22%) AI, bots and ML in their business (Source: Deloitte). What are some AI and robotics applications which are transformational for each aspect of finance and banking businesses?

Photo by  Floriane Vita  on  Unsplash 

Retail Banking  

Verification Methods: Facial Recognition 

With the push for cashless societies, retail banking sees increased emphasis in mobile-centric customer experiences. People expect frictionless fund transfers. This includes payments to merchants, online transactions and peer-to-peer transfers. The verification of these payments is usually through 2FA, OTP, advanced biometric recognition technologies. This includes facial recognition, finger print and iris recognition which are powered by image processing using neural networks. 

 

Channel Improvements: Mobile Applications and AI Chatbots 

Photo by NeONBRAND on Unsplash

 

Customer service is an important realm of retail banking and many have made investments in channel improvement to distinguish themselves from competitors. Typically, online banking is available on websites and mobile applications. Offline, there are also call centers and bank branches. Improvements to customer service and digital banking solutions commonly come in the form of mobile application UI/UX improvements and implementation of AI Chatbots. 

 

With mobile applications, banks can provide customised self-service solutions using simple logic flows. Actions are usually triggered with taps and swipes on the app-interface. This is a popular solution and over the years, banks have created multiple mobile applications to meet each of their use cases, leading to what is known as app fatigue. 

 

Automation solutions such as AI Chatbots have also been proven to be excellent for increasing productivity and service levels in banks. AI Chatbots are intelligent assistants built with natural language processing capacities enhanced with intelligent AI algorithms. Actions are triggered using conversational interface (chat) and there is increasing interest in using voice as a trigger. These Chatbots are built to decipher human languages (oftentimes requests and queries) into computer language, before providing the answers or triggering the actions desired. Another plus point for the AI Chatbot is its scalability. AI Chatbots can be integrated across many channels, resulting in it being a popular solution for banks which already utilize multiple channels (e.g. website, many mobile applications and social media handles). As such, Chatbots neatly fit into a bank’s omni-channel strategy, as it ties in all the different channels that the bank operates. 

 

Investment Banking 

Race to the Most Accurate Algorithms and Fastest Fingers 

Investment banks reap plenty of benefits from technology advancements which improves the data processing due to the nature of the market. Investment managers can utilize softwares using historical data to identify patterns in trade to further optimize their trading system. Most applications of ML and AI in the investment banks are modelled for trading purposes. IB firms utilize automated market data collections to tend towards perfect information; by using algorithms to run thorough market analysis and make predictions for buy-sides and sell-sides, traders can make more accurate forecasts. Another upside of automation is its ability to trade faster than human to secure trades at the precise moment for maximum returns. The future of IB largely depends on the accuracy of algorithms and choosing the right strategies. 

 

Insurance  

Chatbots: Self-Service and Information for Customers and FAs 

Like commercial banks, insurance is a customer-facing industry which benefits from the adoption of AI Chatbots solution. The AI Chatbot serves as a vessel of information and aids users with self-service functions. The use case for chatbot is not limited to policyholder’s use. Agents and financial advisors are also users of the platform as they require information to answer complicated questions their clients may pose.  

 

Fraud-detection  

Machines can perform fraud detection more efficiently than humans with their improving abilities to detect patterns. With the use of AI, insurance companies can resolve claim requests quickly. The overall fraudulent claims will decrease with AI applications. 

 

 

Home Insurance Use Case 

UK-based insurer, Neos Venture uses smart technology to provide 24/7 monitoring system for homes. It is able to detect pipe leaks, and home intrusions. These home sensors will trigger conversations with the home owner when anomalies are detected, provide recommendation for fixes.  

 

Determining Insurance Premium 

Photo by Esmee Holdijk on Unsplash

 

Motor insurers have also used telematic sensors to track drivers and hand out lower premium pricing for “safe drivers”. For life insurance, image recognition capabilities allow data points to be collected on individuals. In 2017, a start-up allowed people to buy life-insurance without medical examination – they only need a selfie. Machines can detect tell-tale signs of smoking history and biological age. With this intelligence, it will allocate scores which will determine the premium pricing. These new applications objectively reward lower risks policies and reduce the hassle of cumbersome medical examinations. 

 

Industry-Wide

Today, most major financial institutions have higher readiness for adopting robotics and AI. Cyber-security continues to be the top deterrence for adoption of technology. This can be overcome with deeper understanding of each institution’s technology infrastructure and taking steps to strengthen the controls of the ecosystem to defend itself.