All financial services players, especially payment platforms, need massive amounts of data. There are various data streams including ATM transactions, POS transactions, data collected for KYC checks, and such others. However, there is still a question mark on whether it utilizes this data to its proper extent.
Data silos arising out of various factors such as employee behavior or organizational structuring continue to hamper firms from fully utilizing the potential of the gathered data. More alarmingly, they can also trip up the organization’s overall business as departments are unable to get the data to required move the deal forward or close it from other departments. Many organizations are already utilizing big data for specific departments.
But can we really afford new formats of data silos?
Times have changed, customer expectations have evolved, and so has the competition in the fintech sector. With virtual interactions on the rise, coupled with the increasing competition, organizations must switch to coupling integration with big data to help gain a market advantage.
And what do the players stand to gain with the integration of big data into their operations?
The biggest gain is likely to be satisfied customers. Financial Institutions generate a lot of data about their customers. Applying big data to this raw data, which can be structured, unstructured, or semi-structured, enables them to efficiently segment their customers into various categories such as age, gender, spending habits, and social class. This segmentation in turn will enable various departments in the FIs to tailor their products as per the customer’s needs and demands. The firms may also be able to spot unnoticed valuable customers and offer them better service, driving higher customer satisfaction while ensuring lower churn rate.
The segmentation will also enable the companies to offer higher-quality personalized services to their customers. With intense competition, firms need more ways to attract new customers and avoid churn. Companies can draw on inferences drawn from the patterns derived from the data gathered from customers to offer more appealing personalized services to their customers. This will be especially useful for companies to gain a market advantage as personalized services are fast becoming a norm, rather than a unique offering.
Not only can the FIs offer better products and increase chances of customer retention, but big data also enables them to study the customers’ behavior across various channels and predict their next actions. In addition, it increases the FIs’ CX capabilities by enabling them to better satisfy the customers across all channels of interaction. It also enables the FIs to get quicker customer feedback instead of traditional (slow and sometimes inaccurate) sample groups.
The patterns may also prove immensely helpful in detecting nefarious activities like fraud, as the company can detect unusual patterns in the customer’s financial behavior and take timely action. These patterns are also immensely helpful in performing risk management, as big data offers deep insights into various facets of the customer’s profile, including spend patterns. These insights help in better credit risk management by enabling the FIs to improve credit models for all types of customers and better management of collaterals.
The insights offered by big data also enable FIs to comply with regulations as they can spot potential breaches and move in time to avoid the damage arising out of the situation. In addition, big data also enables the FIs to efficiently manage their liquidity as it offers better insights on both incoming and outgoing cash flows.
These are some of the major advantages of integration of big data with payment platforms. The integration will see a rise fueled by these advantages as well as due to the rise in virtualization.
