AI and the Future of Banking

CHAPTER 4Key Elements of Banking Analytics

SUMMARY

In this section the current use of analytics in the banking industry will be considered in more detail as an introduction to the topic, before going on to look at the topic of machine learning and AI in more detail in the next chapter.

The examples given are grouped into the four key areas: financial performance management, customer analytics, risk management and operational efficiency.

Of these, the most important is arguably that of financial performance management because it is in effect the ‘engine room’ of the banking organisation. Without effective visibility into the achieved revenue, costs and channel profitability, the organisation is operating blindly. Clear understanding of the financial situation is critical to understanding the potential return on investment for the adoption of new technologies. The other three areas of customer analytics, operational efficiency and risk management are primarily separate functions (although in some cases the risk function is ‘owned’ by the Office of Finance).

The way that the examples have been arranged has been to group each set of analytics and then consider their relevance to different types of banks. A different approach might have been to look at the use of analytics on a ‘bank-type’ by ‘bank-type’ basis, allowing future specific bank-type books to be written, such as ‘Analytics for Investment Banking’, which could have greater depth. However, this book aims for a more generalised ...