Imran Aftab, Co-Founder & CEO of 10Pearls.
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Finance has always been a champion of digital innovation, and the recent AI wave proves no exception. As an industry that is under increasing pressure to deliver faster, more personalized, and efficient digital experiences to customers, embedding cutting-edge technology is a non-negotiable.
As fintechs move beyond AI experimentation to embedding it in their core strategies, the question is not about the value AI delivers, but how it is governed over time. Without clear guiding principles embedded within a central framework, fintechs will swiftly encounter risks from a reputational, regulatory, and security standpoint.
A living framework not only covers all bases, but does so while keeping pace with evolving strategies. It propels, not curbs, innovation—without compromising fintechs in the process.
Striking a Balance Between Fairness and Accuracy
The rapid digitization of financial services also creates more opportunities for potential fraud and cybersecurity attacks. However, ungoverned AI often falls prey to hallucinations and bias—meaning that account holders can be erroneously flagged by the very systems designed to protect them.
Fintechs must ensure AI systems operate consistently and meet performance standards. Poor data management is a cornerstone of ungoverned AI and snowballs into disastrous consequences. It’s not a matter of simply acting in real time, but doing so accurately and fairly. When the data that informs these systems is not managed properly, deployment is doomed to fail.
Consider an AI system misinformed with mismanaged and skewed data that has mistakenly flagged a legitimate, large transaction as fraud based on the account holder’s zip code. Certain demographics are singled out based on inaccurate historical data, which only serves to reinforce bias against individuals or groups. Discrimination not only damages trust and relationships but also has long-term ramifications on an institution’s reputation, particularly as it directly breaks consumer protection laws. Fintechs have a legal obligation to fairly and securely use data across an AI system’s lifecycle, and it’s not the tools brought into question when transgressions arise, but the teams using them.
The consequences compound beyond this. These scenarios create added strain on teams, who then have to intervene, wasting precious manpower and time. Crucially, they also flag serious gaps in the existing foundation. Unmanaged data is a weak spot in a fintech’s digital fabric, making it vulnerable to actual fraud and cybersecurity threats.
A living governance framework counteracts these risks because it requires continuous monitoring, testing, and recalibration of AI models. This enables financial providers to maximize their security robustness on a constant basis while regularly evaluating and updating systems as data and risks evolve. At the same time, bias is rooted out, making way for fairness and accuracy throughout.
Ensuring Explainability and Transparency
Fintechs following a living framework prevent AI from functioning like a black box, where its inner workings are a mystery to teams and users alike. Account holders, staff, and regulatory bodies require reassurance in the form of explainability and transparency around any integrated technology.
Eradicating bias requires understanding how and why an AI tool reached a decision. AI systems are now used in processes like credit scoring, but unfortunately, they are not immune to bias. The ramifications of this are severe: discrimination, particularly against minority groups who are disproportionately denied loans because of faulty AI. Regulations like the CFPB and Fair Lending laws demand explainability and traceability of AI tools used in financial services. They also require that bias be removed from the equation.
In a living governance model, explainability and traceability are ingrained into every use case and workflow:
- Data sources and destinations are clearly logged.
- All model changes, tests, and observations are recorded.
- Decision logic is communicated so that regulators and customers, and not just operators, understand how and why an AI system reached a recommendation or action.
Guaranteeing AML Compliance
Financial institutions are turning to automation and AI to monitor suspicious transactions and activity as part of anti-money laundering systems. However, when AI is not properly overseen or managed, two issues arise:
- False positives: Legitimate transactions are wrongfully flagged, leading to frustrated customers and wasted precious manpower.
- False negatives: Real threats are missed, jeopardizing entire datasets and digital systems, putting the organization’s reputation on the line, and destroying trust.
With a governance-as-guardrails approach, these risks are minimized via well-managed, transparent, and auditable data. Clear alerts are also integrated with immediate actionable insights to ensure swift intervention when needed.
As AI solutions continue to evolve, adaptable, living frameworks become increasingly necessary. These not only protect institutions and individuals alike from potential risks of AI’s involvement, but also provide fintechs with a significant competitive advantage. These frameworks equip them with the means to augment trust and boost their reputation by providing accountable governance, fairness, and transparency, and ensuring reliability and performance.
