Many modern customers choose digital payment over the traditional payment because it is fast and easy. Not only for customers, the financial institutions that offer digital payment services also get many advantages by the rise of digital payments.
According to McKinsey research, digital payments generate roughly 90% of a financial institution’s useful customer data. The data help them to better understanding customers’ needs, creating better products, attracting more customers and proactively promoting new value based on customer interest and preference.
With too much payment data available in financial institutions, the opportunity to leverage that data is world-wide. For example, the country with the forefront of technology innovation such as China and India, experiencing exponential growth in data payments such as account-to-account transfers, instant payments, and pay later. The companies within those countries are generating a treasure trove of transaction data from which valuable insights can be drawn.
Indonesian financial institutions are also experiencing exponential growth in data as the result of digitalizations. However, not all companies are able to unleash the value of their data because they do not have the right data management.
In most cases, data management fail because of data silos. Data silos are a group of raw data that is accessible by one department but isolated from the rest of that organization. Data silos results in a severe lack of transparency, efficiency, and collaboration within that organization. Managing data seems so difficult in business with data silos environment.
In fact, suffering from data silos is only for companies with traditional data analytics tools. The traditional analytic tools are not equipped with the ability to avoid data silos and have poor scalability, then if the data volume is getting bigger exponentially, it would affect the quality of data analysis.
Overcoming the data management challenges in financial institutions with modern data analytics platform
Financial institutions can leverage their data payments for various purposes that may have never been realized. For financial instutions or banking, data can be leveraged for the needs of marketing, customer service, credit risk, to ensure security and avoid fraud.
Here are the examples of data analytics use cases in financial instutions:
- Analyzing data to customize offers and also to identify suspicious anomalies that indicate fraud or account takeover.
- Analyzing data to employ credit risk and support new services like “Buy Now, Pay Later” – an emerging service trend in financial institutions.
- Analyzing data to personalize offers, detecting fraud and improving customer convenience by simplifying authentication such as changing password into biometric login.
To unleash the value of data payment within financial institutions, they should consider to make a shift to modern data analytics platform.
Together with Teradata, PhinCon offers modern data analytics platform for banking and financial institutions. Teradata Vantage is a modern data analytics platform that can unify and analyze any types pf data with high scalability, AI and machine learning powered, flexible deployment options, and no coding required platform.
Traditional data analytics platform generate many reports with multiple forms. In contrast to modern data analytics platform, companies only need one single source to collect data from many sources and analyze it all within one platform. Therefore, modern data analytics platform makes analyst’s job easier and more effective.
You can get further information about the advantages of modern data analytics platform here.
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