Advanced Analytics: The first step towards digitally transforming the banking industry.26/04/2018
Commercial Development Manager for SONDA Latin America.
It is important to understand that DT is a continuous process that requires a cultural change within an organization and does not come to a close. Each company department must be integrated and continuously "digitally transformed", in order to add value for customers, and strengthen its technological tools.
The whole DT process begins when the greatest volume of data relevant to the business has been gathered and can be analyzed. A good DT project can define which features or products should be offered and how should they should be supplied to customers, while taking advantage of the enormous potential that analytical tools can bring to risk analysis, fraud detection and compliance processes in general, which are all key elements of the finance industry.
A study conducted by Salesforce found that 61% of the millennial generation agree with personal data sharing, provided that it results in more personalized services that lead to better recommendations. Therefore, the market demands solutions that use analytical and big data tools, and those institutions that are going to successfully benefit from these processes must understand the power of this data and the risks inherent in its management. This has been achieved by large banks worldwide. Every day they generate enormous volumes of data that serve the organization by creating and improving its products and processes. Meanwhile, they also have powerful tools to safeguard its use and dissemination to third parties.
Sophisticated analytical banking tools can grow revenue and earnings, due to good risk management policy, advanced algorithms that can predict payment patterns, simulate different business scenarios, and complete econometric calculations that improve the efficiency of credit processes, among other factors.
IT suppliers can guide and implement DT processes together with customers by intensively using big data and analytics, while emphasizing the key aspect of data capture and availability. As regardless of how well an algorithm performs, if the data is not sufficiently reliable, the tool will not work efficiently. This is required to meet the demanding challenges of a finance industry that is placing increasing emphasis on the customer experience, and requires ubiquity and speed, as well as efficiency and competitiveness.