The Impact of Collaborative Data Sharing
Welcome back to our continued journey through ‘Collaborative Finance in a Fragmented World.’ In our previous installment, we delved into the nature of data in the financial landscape. We discussed how technological progress has simplified the extraction of value from this data. We explored how collaborative sharing of data is yielding insights that enrich the entire ecosystem—a clear illustration of synergy enhancing the collective; or in layman terms, the whole exceeding the sum of its parts. In this concluding post of our series, we will delve into real-world examples of collaborative efforts in data sharing within different segments of the financial landscape.
Accurate underwriting plays a pivotal role in customer lending, and having a comprehensive history of a customer’s debt servicing greatly improves the lender’s ability to profile and price debt effectively. This necessity has prompted credit bureaus such as Experian, Equifax, and TransUnion to engage in collaborative efforts to share financial data. Their joint efforts have resulted in the compilation of credit reports and credit scores for individual borrowers. An almost like-to-like analogy can be found in the Insurance industry, where companies often collaborate to share data related to claims, underwriting, and risk assessment. This data sharing helps the entire insurance industry get better at predicting and mitigating risks.
Open banking initiatives, particularly in Europe under PSD2 (Payment Services Directive 2), require banks to share customer financial data (with customer consent) with authorized third party providers. These third parties, often fintech companies, can access financial data to create innovative financial products and services. One such innovation comes in the shape of Account Aggregation, where the user can link multiple bank accounts and financial institutions in one place. This provides customers with a holistic view of their finances, including account balances, transaction history, and investments, without the need to log in to multiple banking apps. A natural extension to this holistic aggregation is personal finance management, where the user can budget and track expenses, and set financial goals by categorizing and analysing their spending habits.
Government tax agencies often collaborate with other countries to share financial information about taxpayers with international assets. The Common Reporting Standard (CRS) is an example where countries share financial account information to prevent tax evasion. Then there are government organizations who may not directly engage in collaboration but plays a pivotal role in facilitating collaboration among its member countries and other stakeholders.
One such organization is Financial Action Task Force (FATF), an intergovernmental organization established to combat money laundering and terrorist financing on a global scale. Its standards and mutual evaluation processes encourage countries to work together to strengthen their Anti-Money Laundering (AML) and counter-terrorist financing (CTF) regimes. While FATF does not actively champion operational collaboration, who in the right mind will invite global economic sanctions by not participating wholeheartedly in this endeavour. Such collaborative information sharing makes it possible to track transactions and customer behaviour in real time, which in turn makes it easier to spot possible money laundering schemes.
Finally, the advantages of collaboration in resisting fraud are very apparent. With the prevalence of real-time payments and the ever-present threat of technologically-savvy bad actors, banks are increasingly adopting a collaborative approach to share their expertise and data while safeguarding customer privacy and ownership.
When financial institutions join forces to exchange “hotlists” and “alerts,” they significantly enhance their ability to detect fraudulent activities. If one institution identifies suspicious individuals or transactions, sharing this information with others can thwart similar fraud attempts elsewhere. Secure networks enable the swift dissemination of real-time warnings and updates to these lists, empowering rapid response and reducing the overall risk of fraud.
Taking collaboration to the next level involves training AI models on more extensive databases. This is achievable through data sharing within the ecosystem, where transaction data is carefully anonymized in partnership with industry peers or data consortia. This process enhances the accuracy of machine learning models. Furthermore, the collective wisdom regarding evolving fraud schemes over time can contribute to the continuous improvement of these models in detecting emerging fraud trends.
One organization that has exemplified the effectiveness of data-sharing in the battle against fraud is the National Payments Corporation of India (NPCI), a distinguished institution that has revolutionized real-time payments in India over the last decade. The Enterprise Fraud and Risk Management (EFRM) platform, whose pilot testing began as far back as 2019, has garnered widespread adoption. As of September 2023, over 1200 banks and financial institutions in India are utilizing the platform. Since its inception, this platform has successfully identified and prevented fraudulent transactions amounting to billions of rupees. Presently, it facilitates more than 10 billion transactions per month, spanning various channels such as ATM, POS, Ecommerce and Indian payment networks like UPI, AEPS, NETC, IMPS, BBPS, and more.
RS IntelliEdge™ has been implemented for the National Payments Corporation of India (NPCI) as an Enterprise Fraud Risk Management (EFRM) that is successfully running pan-India. It is achieving an 80% reduction in leading categories of real-time payment fraud for 1200+ banks in India, scoring over 200 million transactions each day.
The EFRM platform is poised to play a pivotal role in fortifying the Indian financial ecosystem in the forthcoming years. Its primary goal is to enhance security and resilience against fraud and risks within the Indian financial landscape. Over time, it aims to encourage Indian banks to share anonymized customer data, fostering the creation of more comprehensive and insightful reports that provide a 360-degree view of financial activities.
This brings us to an end of this series. We had lot of fun in writing this, and we hope we succeeded in regaling you with information and insights in equal measure. Over the multiple posts in this series, we have embarked on a journey through the evolving landscape of digital payments, exploring the transformative power of partnerships, data-sharing initiatives, and the dynamic interplay of technology and collaboration.
As we close this chapter, we invite you to stay engaged with the evolving world of collaborative finance, where opportunities for innovation, efficiency, and financial inclusion continue to unfold. Thank you for joining us on this enlightening exploration, and we look forward to sharing more insights and stories in the exciting chapters yet to come.