Artificial Intelligence (AI) is transforming the financial services landscape, and credit unions are no exception. Its uses, such as chatbots, streamlining credit underwriting processes and analytics to understand member behaviours, can all lead to opportunities for growth and efficiency.
However, the benefits that the technology brings is not without risks and the understanding and management of these risks is crucial to not only long-term sustainability but also to maintaining the credit union brand as one of trust and integrity.
This article outlines the key risks associated with AI adoption in credit unions and practical strategies to mitigate them.
1. Data Privacy and Security
AI systems rely heavily on data especially deep learning models. Often this data can be sensitive personal data and may contain financial information. If this data is mishandled, there is a real risk of data breaches which may not be as easily contained as more traditional data breaches.
To minimise this risk and protect personal data, it is important that data protection practices evolve to meet the changing landscape. This includes looking at policies around what personal data can be used and for what purpose by these AI models. Additionally, it would be important that data breach procedures are reviewed and updated to deal with minimising any contagion risk associated with a breach from using such technology.
Ensure that a DPAI with appropriate depth is completed and that any vendors engaged by the credit union can demonstrate appropriate data protection standards of operation.
2. Bias and Discrimination
AI models can inadvertently learn and perpetuate bias, leading to unfair lending decisions or member service issues. It is therefore important that any AI system which is used would have appropriate human interaction and oversight. For example, the final decision on loan approval would be reviewed by a lending officer or a quality assurance review of decisioning would be undertaken to identify any bias that might have been introduced. It is also important to test the system to see if bias is present.
3. Lack of Transparency (Black Box Models)
Many AI algorithms, especially deep learning models, are difficult to interpret, making it hard to explain decisions to members or regulators. To manage this, consideration should be given to using models that can “explain” using things like decision trees etc. If the use of AI is related to a project, detailed documentation of the project development, the inputs and outputs should be maintained as evidence of the development phase.
4. Regulatory Compliance
The delivery of services to members must be done within a regulatory framework and the evolving regulatory landscape should be considered in the context of the use of AI. This includes things like credit ratings, member privacy, detecting potential money laundering and protection from cybersecurity threats.
Risk management and compliance frameworks should be evolved to include risk assessments on the credit unions use of AI, compliance with regulatory requirements while using AI. The control environment should include both preventative and detective controls to pick up any potential breaches or weak points that might leave the credit union exposed.
Operational Risk and Governance
The rate of development of AI is so fast that many people have commenced using AI tools without adequate consideration of the consequences if it goes wrong. To minimise the damage from errors, outages and so forth, credit unions need to augment a range of policies and procedures covering the use of AI. It is important that roles and responsibilities for using AI tools are clearly defined including permissible and prohibited activities. Also, when systems are unavailable, it is important to have a fall back being the manual processes that served well before AI was prolific.
Final Thoughts
AI has the potential to elevate the capabilities of credit unions and enhance member services. However, misuse or perceived misuse of AI can damage the credit union’s reputation and member trust. Credit Unions need to proactively manage risks related to data, bias, transparency, and governance while actively continuing to be innovative and sustainable. It is therefore important to be open and transparent with members about the use of AI (including providing appropriate opt-outs) while at the same time developing clear AI policies and practices which give staff members the confidence to carry out their roles effectively.
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If you have any queries in relation to this and the impact on your business, please reach out to our GRA Team.
Linked Author: Michelle O'Donoghue
