Why is big data the future of banking?
The Wells Fargo case
As more and more financial operations go digital, banks and financial providers cannot stay back. But with greater technology and optimization it also comes greater responsibility, security-wise. But data analytics can help the banking sector offer better results in investment and other areas. Here we’ll let you know why many banks are turning to big data using one particular successful case, Wells Fargo.
Let’s go through the basics
Big data and data analytics is the most crucial channel of innovation in the investment banking industry. Why? Because it brings with it a wide array of possibilities: it can improve customer experience, personalizing it; it improves service efficiency, and aides making vital business decisions. In a market such as today’s, the burden of rising costs and volatility, companies can use data analytics as a leverage to have better knowledge of the market and for its particular needs.
But, in general, they need to approach third parties. Investment banking companies usually come up with difficulties on how to analyze the data they manage and its performance. So, big data answers to three main issues financial providers face: it helps them optimizing internal processes; improving cybersecurity, and thus, reducing risks; and providing enhanced visibility to daily operations. This means using big data helps them to track their customers’ behavior and predict the best options for them, and let them provide them tailor-made products to meet their necessities.
How has Wells Fargo used big data?
Wells Fargo is the world’s fourth largest bank and it went through a massive change last year as it tried to transform the way the bank approached its data by centralizing it. The bank did this to improve their procedural efficiency, improve consumers’ experience, and be able to comply with the complex regulatory requisites financial companies face.
The plan was initially for three years, but just a year into the plan the bank is already seeing its fruits. They are leveraging the data to improve efficiency, use the data to predict future threats, mitigate their risks, and personalize customer experience. They are using AI to enhance personalization for customers and use machine learning algorithms to have a better understanding of the customers’ interactions with the bank platform. Also, both AI and machine learning are being used to improve risk and fraud detection and compliance.
Usually data is scattered all around an organization in different environments and platforms. What Wells Fargo is aiming is to organize the data and put it into one place (hence, “centralizing”) to control it a standardized, efficient, and secure manner.
What are Wells Fargo plans for the next two years with AI and machine learning? To standardize and automatize most of its model development processes to continue striving in leveraging of big data for the company.
In conclusion, Wells Fargo is the best case of how a concerted strategy of big data analytics can help a company improve its processes and optimize customer experience through AI and machine learning.