Predictive analytics in the financial industry – today’s context and tomorrow’s opportunities

With the advent of IoT and big data, when more and more new data is delivered every day by cloud technologies, businesses are struggling to define a more innovative approach for data usage and analysis. More and more business developers and managers in the financial sector are adopting a historical data-oriented reporting approach rather than use this data discovery to tackle similar problems in the future. This approach includes representation of data, which is stale and does not allow provision of future assumptions based on past experiences. The decisions are made intuitively and are seldom based on solid facts, often resulting in not the best optimal decisions.

Instead, information regarding sales, risk management, transactions and customer satisfaction polls that the financial institutions collect could be used much more effectively for improvement of operational issues and enhanced business performance with the help of predictive analytics.

Predictive analytics – how to use data for future assumptions

Predictive analytics in the finance sector is the art of using massive amounts of both sensitive and insensitive data to find patterns and enable companies to more effectively identify strategic business investments and assess customer relations. At the forefront of predictive analytics lies the assumption of identifying future fields for improvement of services and processes. The opportunities that predictive analytics provide include focus on real-time recommendations instead of static data examination, continuous ranking of possible actions and choosing the best action of all, flexibility through automation of processes.

A Gartner graphic on what predictive analytics is

Gartner explains the four types of business analytics, the dynamic movement from hindsight to foresight

Effective usage of predictive analytics is only possible within data-driven organizations. Businesses in the financial sector have more potential than any other industry to quickly increase profitable investments and improve the effectiveness of their services, if they start to use and rely more on predictive analytics. About 41% of banks use predictive analytics today to turn big data into actionable insights. Among the rising challenges before the banking sector stand the rising customer expectations and rising shifts in customer behavior, higher levels of fraud and increased risk losses. With predictive analytics past experiences are analyzed in order to identify and address exact questions in the future.

Decreased risk losses

The ability to understand and assess underlying fraud risks or credit risks stands at the core of the success of financial institutions. Credit scores, insurance claims and collections are an example where predictive analytics can be used. Credit scores are used to assess the likelihood of the buyer persona of default for purchases, while predictive analysis in collections is used to establish a better understanding of banks’ portfolio risk and improve the productiveness of the collections process.

Quick detection of fraudulent practices

Digitalization has made fraudulent practices in financial institutions more common nowadays. It raises the need for banks to adopt more intelligent systems to deal with hackers and cyber criminals. The role of Analytics is to recognize frauds that are not obvious for organizations, while predictive analytics are implemented to analyze further the likelihood of fraudulent activities and detect them in an early stage.  This is done throughout data integration and utilizing and processing of unstructured data which helps identify patterns and repetitive behaviors.

Improved customer retention

89% of banking and financial markets CEOs claim their top priority is to better understand, predict and give customers the quality of services they need, as stated by IBM’s 2010 Global Chief Executive Officer Study. With predictive analytics banks and financial institutions are able to make in-depth analysis of the customer base and predict which customers are likely to defect before they end their relationship with the bank. It examines customers’ service performance and spending and targets the best product offerings to the most appropriate customer group. Banks can thus build solid relationships based on loyalty and continuous improvement of the customer experience.

According to statistics from the Global Consumer Banking Survey from 2014 the confidence in the financial industry has decreased with 19% on a global scale and with 22% in North America.

Predictive analytics graphic

Change of consumer confidence in banking institutions for the course of 12 months throughout 2014

Conclusion

The financial institutions are among the most advanced users of predictive analytics. With predictive analytics banks and other financial services institutions are enabled to make more data-oriented decisions to improve techniques of fraud detection, increase customer retention and increment risk control. Apart from that, predictive analytics are used and will continue to be used in many more industries such as insurance, health care and manufacturing which process an enormous volume of raw data every day.