Artificial Intelligence/AI has been jogged out as a trend to watch for many years now. But there’s a good reason to talk about it in the context of 2020. And that’s thanks to developments of machine learning models for identification that are capable of, among other features, verifying a person’s identity in seconds.
Why the excitement? It means that AI is becoming an increasingly integrated part of the financial system, a seemingly small and geeky detail that could have long-term consequences.
Sure, we’ve already got plenty of examples of fraud prevention tools using AI: speech recognition, sentiment analysis, image recognition, and natural language processing are no longer earth-shattering new. But these tools generally process the data very slow and send back results in hours, sometimes in days.
While it’s good to go for the tasks for which small delays are forgivable. But this latency is a kiss to death for more intricate tasks such as online identity verification. Face-based authentication, for instance, needs to instantaneously and continuously recognize the face for fast payment processing at the checkout, not leaving any chances for a delay. But this latency is a major pain in simpler authentication too.
The main trouble
Not so long ago, I tried to create an account on a freelancing website, that asked me to verify my identity through a selfie. The idea is that it will match the picture on my ID with the selfie. A very simple task isn’t it? You would think so. But even this seemingly simple task meant waiting 3 to 4 days to get my identity verified. I can’t find any reason to wait 3-4 days for the problem that isn’t from my end.
The task has to be completed at lightning speed so that no one waits before accessing your services while accuracy isn’t compromised. But because machine learning and artificial intelligence bring accuracy and speed to the verification process, the landscape is changing.
If I were to build this solution, I would employ machine learning models that authenticate a person in real-time. No extra human effort is needed and verification without any latency. It would have been a much customer-centric and cost-effective solution.
Fewer delays, more accuracy
While manual verification takes a lot of time, have you ever wondered how AI performs the same task within seconds? To understand, let’s look at how AI could ease the task of verification.
For verifying the identity of a person these two tasks are necessary; ID validation and facial recognition. To validate the ID document, the main task is extracting the information and verifying that MRZ code is original that too from a scanned document or a picture. Machine learning and computer vision could help us in solving this issue, while trained on intensive data these models could reduce the overhead of manual verification and deliver accurate results. While the deep neural networks for face recognition could help us to verify the face of a person quickly.
As you might have guessed by now, that the whole process would take a lot of time while performing manually, and with thousands for verification to process daily this would at least take hours, if not days. This is why the development of AI and machine learning to solve this issue is vital and is already being adopted by businesses.
The Future is Now
Hypothetically speaking, we can also use machine learning and deep learning models for fraud prediction before it happens. By monitoring online transactions and based on previous data patterns for fraud, the industry can even advance further by mitigating fraud attempts. While all these tempting possibilities are on the verge of becoming reality, 2020 could well be the year, when AI engineers unleash true artificial intelligence. The only thing that is stopping people from using cloud gpu for deep learning is cost. Who knows what the future holds?