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Data integration for regulatory reports improves Banks’ approach to Data Centralization
For the past few years, there has been a drastic change to Indian Banking. Right from the rise of customer-centric banking products to the inclusion of new payment modes, more and more activities aim to improve customer experience, promote financial inclusivity, and increase informed lending.
Because of this shift, its dependency on technologies has increased, directly impacting the number of digital services and data generation. Subsequently, Banks need to manage their assets and liabilities better. In doing so, they can proactively adopt data systems that align with future goals.
In digitalization, regulators see scope for huge improvements in risk monitoring. Instead of just being responsible for customers’ misdoings after the deed is done, a comprehensive approach to data-first banking helps with proactive risk management.
Let's how policies that promote digitalization like the mandate for Automated Data Flow (ADF) benefit Banks in the long run.
Automated Data Flow: One Answer to All Banking Reporting Woes
RBI introduced the initiative of Automated Data Flow (ADF) in 2010. The Reserve Bank of India's (RBI) approach paper outlines a framework for putting in place a Central Data Repository (CDR) with various layers for data acquisition, data integration, data conversion, data validation, and data submission.
Since there is heavy diversification of systems, Banks require software experts that can be either third-party vendors or in-house IT teams. The data used in ADF reporting also works for detailed research reports such as Ind AS, IGAAP, and more.
Consequently, the process of centralizing data offers Banks a boost for internal data requirements such as cleansing, democratization, and research. Considering these benefits, Banks can preplan their data goals while implementing Automated Data Flow (ADF) method.
CRisMac ADF: Build a Centralized Data Repository for Regulatory Reporting and More
Even with deadlines in place many banks are yet to become ADF compliant. For this, D2K Technologies offers an out-of-the-box product based on its 20+ years of research in Banking Technology. Our product CRisMac ADF is created by banking technology consultancy experts, specializing in a comprehensive approach to data.
Our new age data-flow model is an enterprise-wide solution that implements the prescribed Centralized Data Repository (CDR) concept. Our R&D for the product is a continuous process based on the expertise gained while building the solution for several Banks and a host of successful ADF implementations.
CDR is the main component facilitating internal reporting, as well as regulatory reporting for RBI, Ministry of Commerce, SEBI, and other regulatory requirements. Here, data is principally sourced from Core Banking Systems (CBS) and peripheral transaction systems like Asset Classification, Treasury, External Data Providers, CRM Tools etc.
The solution also accommodates manual entries through Gap Data screens for missed/recalculated data. It is equipped with MOC screens for changes suggested by auditors. With these features, a proper audit log is maintained for auditors and internal supervisors.
Why Choose CRisMac ADF to Automate Banking Reports?
In a nutshell, the implementation of Automated Data Flow (ADF) is designed to make the process of reporting easier and quicker. With minimal manual intervention, the reports would be in line with banks' financial statements ensuring consistency and comparability.
Many Banks are investing considerably in the ADF method, but consulting experienced techno-functional bankers helps improve efficiency and prepare for oncoming disruptions.
Our Banking Technology consultancy experts are a team of dedicated former banking professionals who have worked on enhancing banking technologies for 20+ years.
Today, products created by D2K Technologies are active in 8 out of 10 top banks in India.
Get in touch with D2K Banking Fintech Consultancy Experts for more information on roadmaps for deploying technologies for comprehensive data analytics.
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