One of the biggest concerns companies raise about the use of AI in systems is its transparency. The black box problem, as it is known, remains one of the technology’s biggest obstacles. The idea that you do not know how a model arrived at a decision leaves many companies concerned about how trustworthy that end product is. When a system flags a document, a business should be able to see exactly why it has done so through a clear process and trail.
Regulators are increasingly becoming aware of this issue. Being able to say ‘the AI flagged it’ isn’t an audit trail, and when regulators put pressure on, it won’t be sufficient. Across regulated and compliance-heavy industries, businesses are increasingly being probed about who is accountable for automated decisions and what evidence can support it.
Holding AI Adoption Back
For many companies, this is one of the reasons they still hold back on AI adoption, and they are right to want to operate with caution. Trust is a big concern to have to overcome, but it is something that we have made a key principle at eyeDP.
AI solutions need to use accountable systems that are regularly updated and that always maintain a human in the loop in key situations. Responsible AI use requires constant training and development. It needs a clear framework for performance monitoring and tracking accuracy. At eyeDP, we regularly test our systems and continuously train them to keep up to speed with new document types, regulations, and fraud techniques.
Remaining Accountable
One way we do this is through our Accuracy Lab. This benchmarks our AI systems against human controls on a weekly basis. The purpose of this is to be totally transparent with our partners about where our system delivers and where it is still developing and learning. We benchmark around 95% extraction accuracy and continue to achieve that. We want our systems to be accountable, with transparency being one of our essential pillars.
We also emphasise the importance of a human in the loop. Our platform will flag complex or ambiguous cases for manual review, ensuring human judgment stays a part of the process. Compliance officers can see exactly what has been triggered, and, if they feel it is appropriate, override the AI decision. This creates a clear record of not just what the system decided, but also how a human has responded. All of this, combined with the analytics and risk scoring provided, means businesses have everything they need to understand verification decisions.
Leaving a Trail
In turn, this also provides a trail. Every action taken on the platform is audited and stored for six years, so if a regulator asks why a decision was made, the answer is easily available. Companies can instantly access documents and see how they were verified, and download a full evidence summary of every completed check and any flags raised.
Having a trail is half of the battle. The other half is having the confidence to stand behind it, and this only comes from working with systems that you know are dependable and are regularly tested and improved upon, just like ours is at eyeDP.
Future Scrutiny
The question of whether you can trust the AI they are using is one that compliance teams and businesses should regularly ask. Regulators are only moving in one direction when it comes to AI, and expectations around explainability and trust are only going to continue to grow.
Realistically, it is not a question of whether your use of AI is going to be scrutinised, but when, and whether or not you are going to be ready for it when it is. The best AI systems are the ones that are built to be challenged, and eyeDP’s platform was built for exactly that kind of scrutiny.
