Using a big data approach to overcoming banking regulatory requirements

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IBM RegTech Innovations.

Asset liability management (ALM) and liquidity risk (LR) are top of mind for banks as the pressure from today’s regulatory environment heats up. Interest rate risk in the banking book (IRRBB) guidelines bring ALM analytics much closer to market risk standards and add more scenarios, so banks must dive deeper into the risks embedded in their loans, deposits, and derivatives. Meanwhile, a great push for granularity in LR underscores a need for robust analytics that can scan, analyze and report on tens of millions of records so that precise cash flows are generated for each position.

Regulators are no longer willing to accept ALM and LR metrics once a month. With a daily view of LR being the new standard, it’s no wonder that banks are asking how this is even possible. The data they must analyze is overwhelming. Many are wondering if it is even possible to overcome these regulatory requirement nightmares.

Regulatory pressure is creating a financial data analytics crisis

Banks know they must be ready to provide regulators with a quick response to regulatory stress tests. They have a critical need for technology that can provide insight into risks embedded in their banking books. But the sheer amount of data in all its forms that must be analyzed is a sticking point. Here are a few examples:

  • A large European bank needed analytics to calculate IRRBB scenarios from 10 million records.
  • Another European institution sought analytics that could do daily LCR/NSFR calculations for all 20 million of its positions individually.
  • A regulator who is concerned about a housing bubble is forcing a financial institution in the Middle East to calculate ALM metrics on every single mortgage in their retail portfolio, which has millions of mortgages.

ALM and LR analytics require large volumes of data that traditional infrastructure struggles to handle. Financial institutions then must pool data and use other tricks to push data through existing ALM systems to get weekly results at best. Currently, they often use pre-aggregation techniques to circumvent technological bottlenecks, which can hide nuances embedded in the data.

So, is there a solution? Yes. Continued innovation in big data technology makes it possible to extend it into new sectors, such as risk management. The result is the ability to compute risk analytics on larger volumes of more complex data in a much shorter timeframe.

Integrating big data technology with risk management for a complete solution

Big data technology is transforming the banking industry, delivering faster, higher quality results at lower costs than traditional approaches. Automation, artificial intelligence, and cognitive analytics are driving significantly improved metrics and reporting and are being used to create innovative systems that transform raw data for cognitive and analytic processing.

With such innovative big data technology, it is possible to scale up the computational power of risk management cost-effectively. Decision-makers can ask more complex questions and get better answers faster when developing new business strategies. But most importantly, risk managers are able to understand nuances in the data because the need to pre-aggregate data is eliminated, and risks are exposed for analysis.

The benefits of innovation

Big data analytics can be up to 15-20 times faster and can deliver results using a full record set without pooling. Banks can now have ALM and LR analytics a lot more frequently, and they can be more confident because results are calculated at each individual position.

Another key benefit of the innovative combination of big data technology with risk management is its scalability. The bottom line is this: the technology can handle monumental volumes of data. If results must be produced faster, they can simply add more computing power. Furthermore, it is possible to trace every record throughout the lifecycle, which is critical for meeting demands for daily LR analytics.

Combining big data technology with risk management and analytics delivers powerful and flexible reporting tools that risk professionals need to answer questions right away, not after an overnight process. It is possible to test regular risk factors and get insights into unforeseen events and shocks that are not regularly observed in the market. Risk managers can ask new questions, think about new scenarios and uncover risks. In the not-so-distant past, that just wasn’t possible. When big data technology and risk management are working together, the pressures of regulations and regulators ease, and risk managers get answers faster—and they get more done.

Put innovative big data and risk management technology to work for you

When it comes to something as important as managing ALM and LR regulatory pressures, don’t settle for data pooling, pre-aggregation, or solutions that are not designed for big data and risk analytics. Learn more at

Global Head, Customer Solutions Group, Risk & Compliance, IBM Watson Financial Services

Luis Matias

ALM & Liquidity Pre-sales product lead, IBM Risk & Compliance

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