If you’re a FinTech organization operating in the United States, then you’re facing a constantly evolving maze of US fintech regulations and compliance mandates regarding artificial intelligence. The failure to comply can lead to serious legal, financial and reputational consequences, including regulatory fines and legal penalties, reputational damage, operational disruptions, lawsuits and liability issues, and loss of market access.
Together these rules determine whether your AI can legally underwrite loans. The best and most practical way to address these compliance demands, and still have scope to innovate with AI, is to implement AI models that are both trustworthy and scalable. In this blog, we’ll explore the guiding principles behind those models, and how to successfully implement them in practice.
Ensuring that AI models are trustworthy and responsible is especially important in a sector like finance, where issues can have major impacts on customer’s banking needs and financial wellbeing. From our extensive experience, trustworthy AI should encompass:
Explainable AI is where an AI model can explain its thought process and how it came to certain decisions. This AI model transparency is critical for financial applications such as loan decisions or fraud detection, where regulations like FCRA require any denial of credit to be explained. XAI can highlight the factors behind the rejection, such as debt-to-income ratio, credit history length, and employment stability, and prove that the decision wasn’t based on any protected characteristics.
Connected to the previous point, algorithmic fairness is important to ensure that AI models don’t make decisions that unfairly discriminate against protected groups. Statistical techniques can be used to measure and mitigate bias, and ensure that trend prediction is always ethical.
The cost of a data breach is rising all the time with the average breach in 2024 costing $4.9 million, 10% more than the previous year. Banking and FinTech firms dealing with highly sensitive data and large volumes of funds are particularly vulnerable. AI models should therefore have privacy built in by design, through data minimization, encryption, and secure processing techniques such as differential privacy and federated learning.
An AI model should combine reliability, stability and resilience. This helps keep prediction accuracy consistently high, including in edge cases when the model might be under stress, and ensure that the model can’t easily be manipulated or misled by training data. Adversarial testing techniques can help here, such as white-box fuzzing, where code and data structures are systematically analyzed to uncover bugs and vulnerabilities.
A cohesive data governance framework is essential to realize the value of AI applications. According to Gartner, 60% of organizations will be losing out on AI use case value because their data governance isn’t sufficiently coordinated and organized.
Research from IDC and Lenovo have found that only 12% of observed AI proof-of-concepts make it to wide-scale deployment. But what are the technical factors that make for a successful, scalable model?
Putting the right building blocks in place is organizational as well as technical. This means CI/CD pipelines, monitoring systems, container orchestration and strong governance frameworks. But it also means ensuring the organization is ready to make the most of AI through skill development, change management, and cross-functional collaboration.
MLOps allows an AI deployment to be automated while still meeting compliance requirements, this is thanks to standardized pipelines covering version control, testing, deployment and monitoring. This works in conjunction with automated compliance checks and audit trails, backed up by vital human oversight.
By 2028, outdated legacy technology could collectively cost banks over $57 billion. Modern AI tools can help bridge the gap between old and new infrastructure, breaking down silos through data lakes, encouraging API integration, using hybrid cloud for AI processing, and replacing legacy components with AI-enabled systems incrementally.
With AI advancing so quickly, and regulations evolving at a similar pace, AI models and compliance demands can quickly become outdated. Contractual future-proofing obligations can avoid the risk of being “locked in” to fixed technology that gradually becomes unfit for purpose.
A strong AI Model Risk Management framework is essential, including risk identification, assessment, mitigation, monitoring, and governance. This fintech risk management approach helps promote responsible AI, from development to deployment, and deliver clarity around SLAs, audit rights and model explanations when working with third-party AI experts.
AI engineering can be challenging for any sector, and it can be especially difficult to know where to start. From our experience in successful implementations of AI in finance, we believe these five steps represent a strong way forward:
The capabilities of AI in finance will only continue to expand. In the months and years ahead, we anticipate trends such as more use of autonomous finance agents that can take care of financial management without human input; and on-device, privacy-preserving AI that runs directly on user devices and enables hyper-personalization without central data collection.
But as AI becomes ever more powerful, the deployment and compliance challenges involved in maximizing its potential will continue to increase. This will only heighten the importance of working with a finance AI partner like Ciklum, with our wealth of sector-specific expertise, agile delivery and trusted SMEs.
Get in touch with us today to find out how we can put your trustworthy AI deployment on the right track, now and in the future.