- Organizations in banking, financial services, and insurance are more likely to prioritize current and future investment in AI than overall survey respondents.
- AI-driven solutions can help the sector verify customer identification and assist with fraud detection as well as anti-money laundering and know-your-customer initiatives.
Banking, financial services, and insurance organizations are eager to leverage AI solutions such as chat bots, deep learning, and machine learning. According to GlobalData’s most recent IT Customer Insight survey, organizations in this sector are more likely to be prioritizing investment in AI technology than their counterparts across other vertical industries. As shown below, 63% of respondents in banking, financial services, and insurance currently prioritize investments in AI, compared to only 54% of companies across all vertical markets. Similarly, 64% of financial services, insurance, and banking organizations have prioritized AI for investment in the next two years, versus only 55% of overall survey respondents.
Not only do these organizations have a wealth of data at their fingertips, offering a treasure trove of information that can yield valuable insights, but they also face numerous challenges, such as stringent regulatory requirements and ever-present security and fraud-related threats. As such, banking, financial services, and insurance organizations have much to gain from implementing AI.
The technology can improve a range of horizontal functions, such as customer care, as well as enhance processes that have industry-specific requirements, such as transaction monitoring and fraud detection for banking. For example, in the banking front office, AI can assist with verifying customer identification and providing a more personalized customer experience, while AI-driven chatbots can tackle queries that come in from customers. Behind the scenes, machine learning algorithms can help with security and fraud detection and prevention by identifying suspicious activity or unusual transactions. More specifically, banks are using AI to assist with anti-money laundering (AML) and know-your-customer (KYC) initiatives. In trading, machine learning models are being used by investors to quickly process large volumes of data to evaluate potential risks and rewards.
And while some may argue that financial organizations are conservative in their adoption of new technologies, there have already been several successful implementations of AI by these companies to date. Infosys helped a European bank implement AI to increase automation and reduce its dependence on manual processes that result in human error, all the while improving security. Wipro worked with an insurance company to help them better detect fraudulent claims and speed payments to authentic customers. And in another example, a banking customer used Wipro’s AI-driven HOLMES E-KYC solution to more quickly ensure regulatory compliance, hasten regulatory reporting, and improve the customer experience.
But, despite the benefits and the successful case studies, adopting AI can be a daunting task. To make it easier, customers should work with providers that have a solid track record within their specific industry. Potential partners should be able to cite successful use cases and demonstrate a strong knowledge of local regulatory requirements. Furthermore, they should explore the numerous pre-packaged solutions on the market that can often be tailored to specific needs. For example, KPMG’s AI platform, Ignite, includes intelligent forecasting, LIBOR analytics, qualified financial contract analysis, cognitive vouching, and cognitive transfer pricing.
On the one hand, adopting AI requires a significant investment of time and resources; on the other hand, it’s an investment organizations can’t afford not to make. They just need to do it thoughtfully.