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Implementing AI/ML Models in Banking Compliance: Addressing Hesitancy to Adopt New Technology



Introduction


Artificial intelligence (AI) and machine learning (ML) are rapidly transforming the banking industry, and the compliance domain is no exception. AI/ML models can be used to automate a wide range of compliance tasks, such as fraud detection, anti-money laundering (AML), and know-your-customer (KYC).

However, there are still significant challenges to getting AI/ML models to production in the compliance domain, especially in banks where there is resistance to new technology. One of the biggest challenges is ensuring that AI/ML models are secure and compliant with all applicable regulations. The Financial Action Task Force (FATF) together with other multilateral as well as regulatory bodies have issued guidelines on the use of AI/ML in the financial sector.

Another challenge is getting buy-in from compliance staff. Many compliance professionals are skeptical of new technology and may be reluctant to adopt AI/ML models - partly due to concern that regulators will not accept AI/ML recommended decisions versus the current status quo.


How to apply regulatory guidelines from FATF/FIUs to make ML models secure and compliant


The FATF has published a number of guidelines on the use of AI/ML in the financial sector. These guidelines are designed to help banks mitigate the risks associated with the use of AI/ML, such as model bias, data security, and operational resilience.

Some of the key FATF guidelines for making ML models secure and compliant include:

  • Data governance: banks should have a robust data governance framework in place to ensure that the data used to train and deploy ML models is of high quality, accurate, and complete.

  • Model development and validation: banks should have a rigorous process for developing and validating ML models. This process should involve testing the models on historical data and on new data that is representative of the current environment.

  • Risk management: banks should have a risk management framework in place to identify, assess, and mitigate the risks associated with the use of ML models. This framework should include measures to monitor the performance of ML models in production and to take corrective action if necessary.

In addition to the FATF guidelines, banks should also comply with all other applicable regulations, such as those issued by their local financial regulators.


How to overcome resistance to new technology in the compliance domain


There are a number of things that banks can do to overcome resistance to new technology in the compliance domain:

  • Educate compliance staff on the benefits of AI/ML: banks should provide training to compliance staff on the benefits of AI/ML and how these models can help them to do their jobs more effectively.

  • Demonstrate the value of AI/ML models: banks should pilot AI/ML models on specific compliance tasks and demonstrate to compliance staff how these models can improve the efficiency and accuracy of their work.

  • Get buy-in from senior management: it is important to get buy-in from senior management for the adoption of AI/ML in the compliance domain. This will help to ensure that compliance staff have the resources and support they need to adopt and use AI/ML models effectively.


Conclusion


Getting AI/ML models to production in the compliance domain of banking can be challenging, but it is possible. By following the FATF guidelines and other applicable regulations, and by educating and supporting compliance staff, banks can overcome resistance to new technology and reap the benefits of AI/ML.


Complidata


Complidata is a leading provider of AI/ML solutions for the compliance domain. Complidata's solutions can help banks overcome the challenges of getting AI/ML models to production in a secure and compliant manner.

For example, Complidata's data governance framework can help banks ensure that the data used to train and deploy ML models is of high quality, accurate, and complete. Complidata's model development and validation process is aligned with the FATF guidelines and helps banks develop and validate ML models in a rigorous and robust manner.

Complidata also has a team of experienced change management consultants who can help banks to educate and support compliance staff in the adoption of AI/ML models.

If you are interested in learning more about how Complidata can help you put AI/ML models to production in the compliance domain, please contact us here


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