Overcoming data obstacles in the banking industry with Industry Accelerators in Cloud Pak for Data

By | 2 minute read | April 28, 2020

During these times of uncertainty, all companies are being stressed in new ways; supply chains are being halted with employee sickness, retail store doors are closed to encourage social distancing, and health care facilities are overwhelmed by patient demand.

In the wake of COVID-19, our banking clients are likewise looking to data science and AI to address four specific challenges: providing an extreme customer experience, mitigating operational risks, reducing operating expenses, and maximizing resource efficiencies.

Extreme customer experience

Banks across the world are seeing a surge in customer support requests as more users shift to digital interactions. This can cause stress on core banking infrastructure while banks build and maintain digital channels. Coupled with economic uncertainty, banks are expecting an influx of loan refinancing requests as loan default rates become more prevalent.

We’ve been seeing a push towards personalization over the past few years, but now more than ever, this data transformation is critical to maintain customer relationships and market share.

Mitigate operational risks

As some employees’ transition to work-from-home, banks are seeing a potential increased risk of loss for failure in internal controls and policies. With bank branches closing to minimize person-to-person contact and reduce unnecessary operating costs, ATM usage is on the rise with a need to access cash. Accelerating digital transformation to drive digital customer relationships, predicting cash-outs and optimizing cash replenishment are critical to meet this new demand. With electronic sharing becoming commonplace – including documents, emails, and other personally-identifying information (PII) –  data privacy threats need to be detected and mitigated actively to ensure compliance.

Reduce operating expenses

With an influx of remote users, network capacity is being tested and strained. Being able to proactively monitor outages and detect outliers can help maintain a seamless remote transition. Given the current economic uncertainty, we’re also seeing an increased stress on liquidity, revenue and expense management. This escalates the need for accurate forecasting and optimized balance sheets to ensure every dollar is accounted for.

Maximize resource efficiencies

At some workplaces, employee time is actively being reprioritized towards personal matters, and hiring freezes are affecting team expansion. With a push towards AI and automated machine learning to solve these new changes, some banks may lack the tools and expertise to maximize efficiency.

The Data Science and AI Elite team has various resources and tools to help banking clients worldwide tackle these challenges through remote engagements.

We’ve built a series of repeatable machine learning assets we call Industry Accelerators on Cloud Pak for Data around common use cases we’ve observed throughout the financial industry. These include: dynamic segmentation, cognitive classification, identity resolution, offer affinity, customer attrition and cognitive controls.

See how we worked with Argentina’s Banco Macro to predict customer behavior, which has the potential to increase campaign response rate by up to four times.

Watch the video:

Interested in learning more about the Industry Accelerators but need a little help in getting started? The DSE team can plan, co-create and prove the project with you based on our proven Agile AI methodology.

Please join Rajiv Chodhari, Tim Davis and me for an upcoming webinar on May 4 from 1 – 2pm EST to learn how you can proactively harness the power of data science and AI across the financial services industry: banking, financial markets and insurance.

For more information on the DSE team, visit: http://ibm.com/community/datascience/elite.

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