The need to use AI in BFSI is growing. That’s because customers prefer to do everything either online or from their mobiles instead of going to a branch office. You must therefore understand their preferences in order to offer personalized service. Only then can you improve customer satisfaction and retention levels. Add to that a complex regulatory framework to comply to and you have a perfect case for AI.

There are several ways to integrate AI in your business strategy. We’re listing 8 of them below.  Alternately, you can also listen to a webinar on the topic and an expert talk on Polyglot computing and insights driven architecture followed by a demo by clicking here.

1. Chatbots or voicebots for customer support: Many banks and insurance companies have already implemented chatbots to enhance customer experience. The benefit of chatbots is that they’re available 24×7, and are easy to use because they use an interface that customers are already familiar with. Both aspects help enhance customer satisfaction.
2. Robo Advisors for Financial Products: An online platform that uses algorithms to automatically create portfolios for users based on factors that investors would use like risk tolerance, income, etc. That’s why they can provide financial advice and manage investments with minimal to zero human intervention. They can also re-balance a portfolio, reinvest dividends, and perform tax loss harvesting measures for their clients.
3. Fraud Detection and Prevention: Fraud attacks are getting more sophisticated and increasing in magnitude. Fraudsters are using increasingly advanced techniques like distributed networks and even machine learning to detect vulnerabilities. Since it’s not possible to keep on adding manpower indefinitely to detect and block security attacks, machine learning can be used to automate the process.
4. Personalized Financial Services: Companies can offer personalized financial advice to users by using robo-advisors. They can monitor a user’s goals and suggest which stocks or bonds to buy or sell. It helps improve customer experience because everyone gets personalized attention irrespective of their risk appetite.
5. Robotic Process Automation: As the name suggests, this is the use of software robots to take over high volume transaction processes and repetitive tasks. This saves time, enhances efficiency, and increases accuracy. RPA can be used to automate back-office work, business processes, IT support processes, etc.
6. Smart Wallets: Another powerful use of AI is in making wallets smarter. By adding intelligence into wallets, BFSI companies can offer smart services like chat, booking of bus, cab, events, movies, utility bill payments, etc.
7. Insurance Under-Writing: Measuring the risk exposure to determine the premium to be paid by a customer is becoming an increasingly complex task for underwriters. AI can help make faster and more accurate risk assessment and pricing.
8. Compliance Monitoring: BFSI companies spend a lot of money to ensure compliance of their records, contracts, loan agreements, etc. with the law. AI can help examine such documents and flag potential issues in seconds, which would otherwise take a lot of time.

Implementation Challenges

As AI is a relatively new phenomenon in BFSI, finding similar use cases can be a challenge. Another challenge is to put data in a structure that can be understood by the AI system. It doesn’t end there, because an AI system would require training before it can start delivering results. A chatbot for instance will not be able to interpret questions correctly in the beginning, so companies would need to use human support to the AI system so that it can learn and start providing the right answers. This is time-consuming and depends upon the volume of data. Lastly of course, is the fact that humans still prefer to interact with humans, so your challenge is to make your chatbot as human as possible!

In Conclusion

AI and cognitive technologies are becoming increasingly important for the BFSI segment, but not everything will be relevant for everyone. So, organizations need to first determine which BU would benefit the most with AI. Is it marketing, customer support, back-end BPM processes, security and compliance monitoring, or something else?

Next, identify and prioritize what data is important for your organization and how’s it being generated. Finally, prepare an implementation roadmap, incorporate your learnings from the initial success stories of others, and start experimenting!