Designing for operational resilience in banking

“A resilient financial system is one that can absorb shocks rather than contribute to them”

This paper talks about operational resilience based on identifying critical banking services and ensuring that these are resilient to on-going threats. It also discusses the potential uses of next generation technologies, and lessons from other industries to support an organisation-wide resilience programme. It includes perspectives from senior C-level execs within banking and an overall sense emerging that a bank strongly designed to be operationally resilient, could truly differentiate itself in an industry reliant on customer trust.

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Some key take aways from the white paper include:

Challenges in taking a critical services based approach to operational resilience

  • Defining the most critical banking services in a bank
  • Global banks may have different business service/product mixes in each country
  • Not all financial services firms roll up or aggregate at a business service level
  • Legal & regulatory challenges arising from the cross-jurisdictional nature of business services
  • Dependencies on systems that support several critical services across different geographies

Banks that do decide to take a critical services based approach should:

  • Map systems and processes to critical services
  • Map services to threats and impact
  • Establish tolerable thresholds for service outages
  • Test for established thresholds for service downtime
  • Use of next-gen technologies to proactively assess and mitigate threats

Best practices for firms that adopt service-based approach to operational resilience

  • Establish executive sponsorship and identify senior managers who will own and help proliferate this new approach

Several AI and ML technologies hold promise to test operational resiliency of banks

  • Averting service disruption – Investment banks are using concepts such as ‘unsupervised learning’
  • Predicting ATM outages – Predictive analytics can help banks monitor ATM service performance when a failure might occur and even improve ATM availability
  • Reducing bank fraud – AI/ML capabilities can be used with deep data analytics and geographical data to identify fraudulent transactions in real-time
  • Eliminating operational risk – AI and ML-based visualization tools can analyze historic data to uncover root causes of disruption

Finally establishing executive sponsorship very early on in taking a critical services based approach as well as ensuring the cultural aspects of such a change in a bank are well addressed, are extremely important. Modernizing legacy systems over a period of time and moving towards a cloud based eco-system enabled by an agile way of working, would serve banks well in making this transition.