Data Analytics: the next move for transaction banking

Transaction banking has made remarkable progress in recent years. The easy availability of new tools and technologies has improved operating efficiency and accuracy.

The challenges of increasing volumes, evolving regulation and growing competition from new market players, complex customer demands. These are collectively require new and innovative business models to drive growth and performance. This can only be achieved through enhanced insight into excellent customer engagements, better relationships, across all lines of business products and geographies, as well as an enterprise-wide view of transaction flows, revenues, costs and liquidity positions.

Hence here is a need of “predictive analytics” that helps to interpret data patterns to predict future behavior and thereby arrive at better decisions which can be implement for better performance and mitigate the risk.

Here is CAPI model to understand this mechanism in better way:

Capture: capture the data for various attributes customer, business product , geographies, regulatory & industries etc.

Analyze: use Data Analytics tools to analyse the capture data for predictive analysis and forecasting

Predict: use the power of technology (Data Analytics tool R, Python etc) to present the information in intuitive way

Implementation: Implement the outcome of the analysis to reap the maximum benefits of the tools and technology