How Signzy Replaced Legacy Banking Processes with AI-driven Technology
Introduction As Andrew NG phrases it perfectly “AI is the new electricity”. We at Signzy are true believers of this and have witnessed what AI could do, how deep its impact can be and the drift it has brought in the way we approach our problems. Being in the finance and regulatory domain we have been identifying a series of our customer problems which could potentially be addressed through AI. In this PPT we will take you to behind-the-scenes of our state-of-the-art system and how we tackled the problem, ultimately overpassing the targeted accuracy required for real world use.
Knowing the beast we are to fight The idea is to deploy the pipeline into financial institutions with all possibilities of input variation and yet it should surpass or at least be equivalent to accuracy of a human being. The solution is to work on data which arrives from the most rural parts with pics taken from even 0.3 MegaPixel cameras and travelling over a dramatically slow connectivity. We knew the toughest challenge was to cater to variations that could arrive in inputs. But at last We were able to get desired training sets successfully, which was half the problem solved.
World is not the cozy laboratory, we know that! • Our target was to create the architecture which could be more than 99% accurate and yet be fast enough to make an impact. • You can’t reject an application by an old rural lady, who has brought you a photocopy of printout which in turn is obtained from a scanned copy of a long faded PAN card. We took it as a challenge to create the system so that it can help even the rural Indian masses.
Creating the technology ď ś Baby steps ahead We tried out various approaches for solving the problem. Firstly we extracted features using Histogram of Oriented Gradients (HOG) feature extractor from OpenCV and then trained a Support Vector Machine (SVM) classifier on top of the extracted features. The results were further improved by choosing XGBoost classifier. We were able to reach about 72% accuracy. We were using Scikit learn machine learning framework for this.
Not enough, let’s try something else In our second approach, we tried ‘Bag of words’ model where we had built a corpus containing unique words from each identity card. Then we feed the test identity cards to an inhouse developed OCR pipeline to extract text from the identity card. Finally we input the extracted text to a ‘Naive bayes’ classifier for the predictions. This method boosted the accuracy to 96% . But the drawback of this approach was that it can be easily fooled by hand written text. *Many more approaches were also followed*
Hit the right nail kid, treat them as objects The final approach is where the novelty of our algorithm lies. The idea is to use an image object detector ensemble model for image classification purpose. For eg. the Aadhaar identity has Indian Emblem, QR code objects in it.Â
Good work lad! What next? • While the pipeline we had come up with till here has a very high accuracy and efficient processing time, it was yet far from the a productionised software. • We are clearly seeing the impact deep learning can have on solving these problems which we once were unable to comprehend through technology. We were able to gauge the huge margin of enhancement that deep learning provides over traditional image processing algorithms. It’s truly the new technological wave. And that’s for good.
Contact Details About Signzy: Signzy helps financial institutions transform current semi-manual processes into real-time digital systems, using Artificial Intelligence and Blockchain. This ensures that the new processes are user-friendly, yet secure and compliant. Bangalore office BPL building, 11th KM, Arakere Bannerghatta Rd, Bengaluru, Karnataka – 560076
Email : reachout@signzy.com
Mumbai office “Rise” 1902, 19th Floor, Peninsula Business Park, Tower B, Lower Parel, Mumbai – 400013
Website : https://signzy.com/
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