Predictive analytics in the financial industry – today’s context and tomorrow’s opportunities
In the world of IoT and big data, when more and more data is collected and processed every day, businesses are still struggling to define an efficient approach to make use of all available data.
Typically, business owners and managers in the financial sector use a historical data reporting approach, where they analyze the available data to reflect on past performance instead of using it to predict future performance. This approach does not allow the provision of future assumptions based on past experiences. The decisions are made intuitively and are rarely data-driven.
Information that the financial institutions collect regarding sales, risk management, transactions, and customer satisfaction polls could be used much more effectively for the improvement of operational issues and enhanced business performance with the help of predictive analytics.
Predictive analytics – how to use data to make data-driven 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 to improve services and processes.
The opportunities that predictive analytics provide include focusing on real-time recommendations instead of static data examination, continuous ranking of possible actions and choosing the best action of all, and flexibility through automation of processes.
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.
According to IBM, 27% of banks and financial markets pilot and implement big data activities to turn data into actionable insights and then into profits. Among the rising challenges of the banking sector stand the increased customer expectations and shifts in consumer behavior, higher levels of fraud, and increased risk losses. Through using predictive analytics, they can analyze historical data on particular situations in order to identify and address similar events 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 cybercriminals.
The role of analytics is to recognize frauds that are not obvious to organizations, while predictive analytics identifies the future likelihood of fraudulent activities and detects them at an early stage. There is usually a lot of unstructured data in any organization that can be cleaned, enriched, and used to identify patterns and repetitive behaviors, along with some external open data.
Improved customer retention
Banking and financial markets CEOs claim their top priority is to better understand, predict, and give customers the quality of services they need. To accelerate digitalization, financial institutions should adopt an architecture-led transformation combined with re-imagined workflows and operating models, according to IBM’s 2022 Global Outlook for Banking and Financial Markets.
With predictive analytics, banks and financial institutions are able to make an 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.
“Trustworthy, transparent models are critical to our success and really go back to our culture and key tenets to serve our customers.”
Manav Misra, Chief Data and Analytics Officer, Regions Bank
This is how AI comes into play in the financial and banking sectors. With the increased use of digitized services, customers expect an instant reaction from the providers. Properly deployed AI can analyze the interdependencies among variable economic conditions, modified human decisions, completeness of data sets, and accuracy of algorithms to meet customer demands.
Conclusion
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.
