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Digital Twin Technology for Simulating and Managing Model Risks in Banking and Finance
Digital Twin Technology for Simulating and Managing Model Risks in Banking and Finance

February 6, 2025

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Banks and other financial organizations mostly rely on intricate models for a variety of crucial tasks in today’s fast-paced world. Whether it is credit risk assessment, investment strategies, fraud detection, and regulatory compliance, banks rely heavily on models which are now being powered by advanced and emerging technologies, such as machine learning (ML) and artificial intelligence (AI) to increase automation and accuracy.

But this also brings with it new difficulties in controlling model risk, which can be brought on by things like incorrect assumptions, incomplete data, or badly executed models.

What is Digital Twin Technology and How It Helps in Banking and Financial Services?

The word “digital twins” was first used by NASA in the 1960s to monitor and optimize spacecraft operations. The Biden administration has allocated $285 million to support digital twin technology in semiconductor manufacturing, recognizing its potential to improve efficiency, creativity, and the resilience of US industry.

Digital Twin technology is a way to create virtual models of physical objects, processes, or systems that can simulate their behavior in different scenarios to understand how it works in a production environment or real life. 

In banking and financial services, it enables institutions to create a digital doppelgänger or identical twin of the financial models, risk assessment frameworks, and operational workflows.

These digital twins driven by AI can for example simulate real-world conditions, market fluctuations, and regulatory scenarios to allow banks to stress test and validate their models for biases, inaccuracies, and ensure compliance with regulatory standards like OCC, SEC (US), and RBI (India). 

With 71% of financial institutions already leveraging Digital Twins (as per this digital twin white paper), the technology is rapidly becoming a must-have for modern banking. But how exactly does it help in managing model risk? Let’s explore.

How Digital Twins Enhance Model Risk Management

Financial institutions rely on complex AI-driven risk models. Therefore, ensuring their accuracy, resilience, and compliance is critical. Digital Twins provide a real-time, data-driven approach to continuously monitor, test, and validate financial models, which helps minimize errors and prevent costly regulatory failures.

1. Stress Testing & Risk Simulations

Financial institutions such as banks must ensure that their models perform accurately under various economic conditions from recession to inflation.

Digital Twins allows banks to test their risk models under extreme conditions by,

  1. Running simulations of financial crashes, interest hikes, and liquidity crises
  2. Analyzing weaknesses in risk models before they result in a failure in the real-world
  3. Ensuring compliance with regulations, such as Basel III and CCAR stress tests.

2. AI Model Validation and Bias Detection

AI-driven risk models can develop hidden biases, leading to inaccurate credit scoring or unfair lending decisions. Digital Twins help by,

  1. Testing AI models, monitor, and validate AI/ML risk in real-time,
  2. identifying biases in credit scoring, lending decisions, and fraud detection models.
  3. Improving decision making before deploying models in real-environment.

3. Real-Time Fraud & Cybersecurity Risk Management

Fraudsters constantly evolve their tactics, making traditional fraud detection models obsolete. With Digital Twins, banks can detect anomalies by replicating the financial transactions and simulating new fraud patterns.

By creating virtual replicas of financial transactions, banks can improve anomaly detection, AML (Anti-Money Laundering) frameworks. and enhance compliance with AI-driven tracking, including cryptocurrency.

For instance, banks across the globe are integrating Digital Twins with blockchain forensics to track illicit transactions and prevent cyber fraud.

4. Regulatory Compliance & Governance

Regulators in India (RBI, SEBI) and the U.S. (OCC, SEC, Federal Reserve) demand transparent, auditable financial models. Digital Twins simplify compliance by enabling banks to,

  • Automate model risk governance for regulatory audits.
  • Simulate compliance scenarios to assess policy impact.
  • Create a continuous feedback loop to refine risk models.

Some Real World Examples

Several banks and financial institutions are already leveraging digital twin technology to improve their operations and manage risks. Here are a few examples:

1. Atom Bank: Enhancing Financial Planning with Digital Twins

Atom Bank partnered with Durham University to develop a Digital Twin of its operations, leveraging advanced statistical modeling to simulate financial processes.

This allowed the bank to optimize resource allocation, refine pricing strategies, and enhance financial planning. By simulating different market conditions, Atom Bank improved its ability to assess risks, adjust strategies, and strengthen resilience.

The technology also enabled real-time scenario analysis, ensuring faster adaptation to economic shifts. This innovative approach has positioned Atom Bank as a leader in AI-driven financial decision-making.

2. Bank of Montreal (BMO): Optimizing Branch Integration

During its acquisition of Bank of the West, BMO used Digital Twin technology to streamline the integration of 503 new branches.

By creating virtual replicas of physical locations, the bank optimized resource allocation, improved operational efficiency, and reduced integration costs. Remote assessments through Matterport’s 3D capture technology enabled seamless planning and minimized disruptions.

The simulations helped BMO evaluate branch layouts, enhance space utilization, and accelerate decision-making, ensuring a smooth transition while maintaining high service standards.

3. U.S. Commercial Lender: Accelerating Credit Risk Model Validation

A leading U.S.-based commercial lender collaborated with Anaptyss to improve the validation of third-party credit risk models.

The bank achieved a 40% faster model validation process, reducing manual errors and ensuring compliance with regulatory risk governance frameworks.

The technology enabled the lender to test multiple risk scenarios, automate validation processes, and enhance overall credit risk assessments. This not only improved model accuracy but also streamlined regulatory reporting and decision-making, leading to more efficient risk management.

Final Thoughts

As financial institutions increasingly rely on AI-driven models, Digital Twins offer a powerful, data-driven approach to managing risk, ensuring compliance, and improving decision-making. By adopting Digital Twins for model validation, stress testing, and fraud detection, banks in India, the U.S., and other countries can enhance financial stability, regulatory trust, and operational resilience.

With the banking sector evolving rapidly, embracing Digital Twin technology is no longer optional—it’s a necessity for risk-aware, future-ready financial institutions.


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Anaptyss is a digital solutions and business services company based in Alpharetta, GA. The organization delivers digitally enabled, value-led managed services to a diverse clientele in the financial services industry. Anaptyss co-creates innovative solutions to help clients evolve their standalone tasks and processes to fully integrated and versatile functions/CoEs, transforming their business and technology operations. Anaptyss' globally scalable managed services ecosystem, driven by the proprietary Digital Knowledge Operations™ approach, offers clients access to new-age intelligent digital technologies, deep-domain expertise, and top-tier talent.

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