Extracting Value From Customer Data


A wealth of customer information from internal and third-party source could help banks strengthen customer relationships, said Mckinsey.

The firm said banks could gain insights on customers’ personal financial management, for example, where they spend their money, to offer tailored solutions such as suggesting the next-best product to buy.

Furthermore, new products and services such as end-to-end digital lending using the additional information for more reliable credit scoring can be created.

“Incumbent banks have always had access to vast amounts of customer data such as transaction behaviour and payments records.

“The digital world offers many additional external sources of data that could allow banks to deepen their understanding of existing and prospective customers.

“With increased access to more digital platforms, consumers in emerging Asia appear more willing to share data with banks in return for customised offers,” it said.

The firm said globally, banks are relying on national databases such as Aadhaar in India, non-banking partners such as telecommunication or e-commerce companies, and the correlation of publicly available information such as social media and employment records, to develop customer insights.

It said In the first quarter of 2017, India’s Aditya Birla Financial Services launched a digital lending platform with a re-imagined approach for unsecured personal lending.

“The solution allows for loan approval within three minutes, with 70 percent of the decisions made by machines. Such a differentiated customer journey is made possible by an underlying design that leverages instant access to verified data, either from publicly available sources or data shared by customers digitally.

“Another leading non-banking financial company in India used analytics to increase cross-sell in its small and medium enterprise-lending business.

“The analytics-based credit engine and prioritization engine, coupled with an end-to-end sales and loan process, led to over 40 percent growth in cross-selling loans and around 10 percent growth in the overall book.

“In China, Alibaba used its various sources of data to develop a proprietary credit assessment engine called ‘Sesame Credit’, which supports the consumer and SME lending business of Ant Financial (formerly Alipay).

It said Sesame Credit combines traditional sources of information for assessing creditworthiness with data on 300 million “real name” registered users and 37 million small businesses on Alibaba Group sites.

“This extra data includes shopping habits, timeliness of payments, residential status, loyalty to one mobile company, among others,” it said.

The firm said fintechs often rely purely on non-traditional information for credit assessment, global banks usually incorporate these additional variables into their existing credit assessment process to enhance their predictive power.

“Applying analytics effectively along the customer lifecycle will require banks to not only build distinctive analytics capabilities in a central team but to embed them across the organization and into employees’ daily work lives.

“Experience in implementing analytics engines suggests that the hardest part is not the modelling or insight gathering; instead, embedding analytics in daily operations is often a matter of overcoming legacy working styles and a very human reluctance to change,” it said.