“A common sales conversation today tends to look something like this: The banker pulls out a menu of services and unleashes every possible product combination, leaving the customer overwhelmed. The customer might leave with one or two products, as well as the feeling that the banker didn’t take the time to understand and address their concerns.
This initial sales conversation remains a major challenge among financial institutions: Retail customers generally walk out with an average of 1.3 products (1.8 for business customers) — but a whopping 70 percent of these products aren’t even the right fit for them, according to internal research based on client data.”
In fact, according to McKinsey, adding AI to the banking sales and marketing process can lead to an estimated increase of 2.5 to 5.2 percent in revenue annually.
“Arguably, no large financial institution can afford not to integrate AI into its business, but care should be taken to establish audit trails and make the parameters of AI deployment transparent and available for scrutiny.
In broad terms, the use of AI in financial institutions can be categorised into four groups.
The first is in customer interactions and compliance
The second is in the context of financial systems and processes, such as payments[i] and treasury servicesThe third use is for the enhancement of financial products and the financial institution’s business modelThe forth use case is to assist with regulatory reporting or change, including stress testing, ring-fencing”
“A blind spot for risk managers in financial services is itself becoming a risk: Few say they have the know-how to properly analyze the potential downsides of artificial intelligence.
11%percent of risk managers in banking, capital markets and insurance say they are fully capable of assessing AI-related risks, according to a survey of 683 risk managers in nine countries released this week by Accenture.”
“Last year, BBVA worked with a team of MIT researchers to develop a model based on machine learning algorithms that can reduce the number of false positives related to fraudulent credit card transactions by 54 percent. The new approach, known as deep feature synthesis (DFS), facilitated the extraction of more than 200 additional attributes from each transaction, which served to provide a more detailed description of the credit/debit card transaction behavior, thus improving the fraud detection engine results.”