Corner OfficeExpert Opinion

Creating an Agile Banking System in the Post Covid-era

By Sudhir Dhingra

Over the last two decades, technology and data has had a significant impact on all businesses especially in the banking sector. Banks have always been at the forefront of adopting new technology and, true to form, have come out with innovative products delivering faster and personalized services like online, paperless, and branchless digital banking, chatbots etc.

While banks have been busy protecting market share from other banks, innovative FinTech companies have emerged as disruptors in many product segments. FinTech companies offer products like personal and specialized loans, crowd funding and crowd loans, specialized prepaid cards, payment apps, and DMAT accounts etc. These firms offer simplified and faster processing as well as lower costs while, paradoxically, lowering their own business risks. The COVID-19 pandemic has only accelerated the digital push for individuals and organizations.

Globally, over 35% of total cyberattacks are directly aimed at banks and their customers. Consequently, banks must deal with internal and external challenges and threats, both locally and globally, while continuing to provide the online and digital services their clients expect. The modern connected environment brings with it a slew of regulatory compulsions, cyber security concerns, internal data/system security needs along with the need to manage internal culture and productivity at an accelerated pace.

To add to the challenge, the banking industry is one of the most regulated businesses worldwide – not only are banks answerable to their clients and stakeholders but also to regulators in the jurisdictions they operate in. These regulators have been (rightly so) mandating ever stricter guidelines and processes in the aftermath of the 2008 credit meltdown.  Banks today must operate under strict constraints in term of capital ratios and risk management scores – any lapses detected by the regulators have severe monetary and reputational consequences.

This environment mandates that banks formulate a clear strategy to stay ahead and beat their competition. Traditional mechanisms of systems and software development fail to meet their two fundamental requirements- RUN-THE-BANK and INNOVATE-THE-BANK. Banks like they were at the forefront of technology in 1960’s which gave the world technologies like ATM’s & MICR which are relevant even today, must innovate not only to protect their current market share but also to ward off stiff competition by disruptors like FinTech companies. Whether the banks operating platforms have been developed in-house or purchased from software vendors, they are stretched to the edge over last few years trying to cope with changes in the market/regulatory landscape as well as advances in the underlying technologies.

Banks in the 1950’s were the first industry to invest in mainframe computers which eventually was instrumental in bringing the world a modern computer and technology we see today, the digital revolution has resulted in massive amounts of transactional data which are stretching the capabilities of traditional MIS and reporting systems – legacy platforms/applications were not designed to handle such large data volumes to power insightful analysis. In order to unlock the true value of immense amounts of internal and external data generated by the banks, there is a need to come up with a different systems development strategy rather than lumping it all in with legacy systems support – as has been done in the last 20 years. This is an area where predictive as well as prescriptive data analytics using Artificial Integillence (AI) and Machine Learning (ML) fits into the equation. A well thought out systems design can ensure better integration between legacy applications that are largely focused on transactional services and newer technologies that enable an operating environment to deliver data analytics and business insights.

How can Data Analytics, AI, and ML help?

The patchwork solutions and quick fixes implemented by some organizations will not survive in the growing digital world which is seriously threatened by a massive surge in Cybersecurity issues.Banks need modern operating platforms that improve operational efficiency by lowering costs while enabling revenue growth through rapid product and service innovation. The key to these goals are the capabilities delivered by the current data technology toolset – Cloud platforms, ML, RPA, AI etc.

Bank’s operating platforms must increase efficiency and reliability, reduce innovation time and costs while offering guaranteed service levels.

Cloud transformation:

  • Enables usage-based costs through the pay-as-you-go model
  • Reduces innovation time through DevOps automation (CI/CD)
  • Provides enterprise grade levels of Data Security
  • Integrates with legacy infrastructure to build a Hybrid Cloud solution

Robotic Process Automation and other automation technologies provide scalability and cost efficiency while reducing operational risk and freeing up capacity for value added activities.

Data Analytics& Visualization can help data mining in banking leading to:

  • Better Customer Profiling – retaining existing customers with appropriate segmentation, targeting new ones
  • Real time Risk management through effective credit modeling and monitoring, prevention of frauds, regulatory compliance
  • Bank-wide operational efficiency that would manifest as delightful services for customers

Critical to unlocking the data potential of a modern enterprise is an investment in a robust data management platform that manages Data Provisioning, Quality and Governance while unlocking advanced analytics through Data Science.

(The author is Chief Data Officer at OnDemand Agility and the views expressed in this article are his own)

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