- Adoption of artificial intelligence (AI) in financial services is maturing as banks implement it across a range of innovative use cases. A new survey of IT executives in banking finds that 85% have a “clear strategy” for adopting AI in the development of new products and services.
- According to a separate global survey of senior banking executives, four in five agree that unlocking value from AI will distinguish winners from losers.
- But firms are treading carefully, balancing business benefits against regulatory complexity and the need to maintain customers’ trust. Most banks (62%) agree that the complexity and risks associated with handling personal data for AI projects often outweigh the benefits to customer experience.
- As a numbers-based, data-driven industry, the banking sector has provided fertile soil for artificial intelligence (AI). As in other sectors, banks have initially found low-risk and incremental benefits in using AI to automate routine tasks. But according to new research conducted by EIU and supported by Temenos, transformational opportunities for product innovation and new business models are also emerging, making AI a game-changer for banks.
When Fitch, a ratings agency, recently announced an investment in US AI startup Sigma, the two companies set out to illustrate the need for AI to help detect fraud with a striking statistic: less than 1% of money-laundering activity is detected.
An EIU survey of IT executives in the banking sector reveals that fraud detection is the top application of AI by banks (see Figure 1). Banks are reaping the benefits of such applications, not only via reduced losses and more efficient use of resources, but through customer experience too. Mastercard, for instance, uses data on transactions and authorisations to predict and detect fraud more precisely and quickly: reducing false positives means fewer legitimate transactions are stopped, improving customer experience.
But AI’s uses in financial services go well beyond fraud detection. Our survey also reveals widespread adoption and heavy use in areas such as optimising IT operations and digital marketing.
Following the footsteps of early financial AI pioneers such as Chinese e-commerce giant Alibaba, more firms are using data from customers’ digital activity to predict credit risk and personalise services. Machine learning techniques allow real-time analysis of customer transactions to accurately calculate default risks. This, in turn, allows banks to offer cheaper loans. In 2020 Barclays partnered with Amazon in Germany to offer credit to shoppers at checkout, leveraging AI analysis of consumers’ online behaviours to approve loans in real-time.
AI can be deployed within the backoffice to optimise and streamline IT operations, to both support people and fully automate processes. Tools such as chatbots can support balance inquiries and fund transfers, reducing the workload from contact centres and internet banking channels, and giving employees more time for value-add work.1
When it comes to the front-office, AI is boosting digital marketing functions. AI-driven technologies can track users’ actions across websites and social media and help banks to reach potential customers through targeted advertising. By analysing large amounts of data on conversion rates and impressions from digital advertisements, AI can also be used to evaluate the efficiency of marketing campaigns.2
Beyond digital marketing, tools such as conversational bots that service basic requests or “smile-to-pay” identification for frictionless transactions are improving customer experience. At-scale personalisation also allows banks to anticipate customer needs and offer highly-tailored services, leading to better customer engagement, opportunities to up-sell and cross-sell, and new sources of product innovation.3
On the wealth management side, AI is enhancing the investor experience. Based on big-data analysis, AI-powered tools can help to optimise portfolios, analyse market sentiment and events, and generate risk profiles for traders, allowing firms to offer their clients the most adequate investment products.4 Investment managers are also increasingly using AI and automation to mine the large amounts of qualitative and unstructured data needed for environmental, social and governance (ESG) scoring.5