Use advanced analytics to identify mis-selling say consultants

18th December 2018 |   Journal Of Sales Transformation

Consultancy firm McKinsey is advocating using advanced analytics and machine learning to help institutions combat conduct risk – for example, in sales environments – as part of a more comprehensive and cost-effective solution. The approach would help “connect the dots” across individual and team activities by integrating customer and other data.

The consultants argue that connections are often hidden in data that derive from multiple sources. Using advanced analytics and machine learning to mine rich data sets can identify “incongruous sales or transaction patterns, misaligned incentives, and inappropriate customer interactions”. The firm cites various high-profile instances of financial services misconduct in Canada, the UK and the US. These took place in retail and commercial banking, capital markets, and wealth management.
A data model can link sales data at the level of individuals and departments (or teams and branches) with other sources of insight such as customer feedback and product data, according to McKinsey. Connected data might include transactional and sales-performance data, customer patterns (such as portfolio activity for wealth management), and customer intelligence (such as call records to service centres, surveys, complaints) alongside company location and hierarchy information. The data integrated could also include content from chat rooms and instant messaging. McKinsey also advocates the use of natural language processing, a type of artificial intelligence that enables computers to interpret free-form written or spoken comments.

See: The advanced-analytics solution for monitoring conduct risk, November 2018,