Explainable AI Is Driving The Credit Analytics Market Now, Says Matthieu Garnier Of Equifax

Key takeaways

Matthieu Garnier emphasises the significance of data quality in building effective models for credit analytics.
Artificial intelligence and machine learning are rapidly transforming the landscape of credit analytics globally.
Data protection and privacy regulations are becoming increasingly critical across the regions where Equifax operates.
Equifax employs various techniques to clean data and ensure its relevance for accurate analytics.

Matthieu Garnier, SVP Data and Analytics at Equifax, had some interesting insight to share with his audience while interacting with data science students at Manipal Pro Learn campus in Bengaluru. A mathematician by profession, Garnier has close to two decades of experience in the field of data science and spoke about how he attempts to solve everyday challenges in data science and analytics mathematically. Analytics India Magazine caught up Garnier to know more about his thoughts on how artificial intelligence and machine learning are driving the changes in credit analytics. Garnier, who leads a team of 400 people across Equifax's 23 offices across the globe, is charged with handling the company's overall data sets and derive knowledge from these raw data. Q: Fake data just like illegitimate news is a challenge for companies who are handling large data sets, could you tell how Equifax is mitigating this challenge? Data management is very important and you need to have good data to build a good model. We have some techniques to clean the data, identify data that are good by combining techniques through very simple routes to make sure that the data is relevant or not. Q. What are some of the recent trends with regards to credit analytics in India and worldwide? Advancement is the area of AI and ML is happening quite fast, if you ask me about the recent trend then I would say that it falls under three main categories

Data protection and data privacy: In each country that Equifax is operating, including India, the regulators want to make sure that the activities around data are well-known and well-explained. There are regulations around how we use to make decisions around AI and data.
Sophistication in AI: The Velocity and sophistication of AI are increasing and it is becoming more powerful as the systems using more and more data. For instance, earlier in credit when we wanted to build a model on behaviour, we used to take samples from population index, as a result of this we were losing out on a lot of information. Now that there is no limitation on storage or processing, we are able to train our algorithms in a better way.
Transparency: That is in terms of how we make sure that people know about their data, how banks can explain to people when we take credit decision while making sure that all the sophistication that we are building towards data and analytics are explainable

Q. With the onset of GDPR, how is Equifax complying to the rule? GDPR is one of the regulations that we have in Europe and some people say that it is the most advanced one, this is because of the idea of GDPR is to make sure that people can own their own data and give consensus each specific use of data. For Equifax, we need to adjust and adapt our processes and making sure that we are 100 per cent compliant with the regulation. This means that we need to keep in our database the consent of the people and ensure the quality of the data. The positive thing that I see in GDPR is that key players are spending more time on data protection and ensuring data quality. Q. According to you how are AI and ML driving the future of credit risk? All industry is using AI and they are trying to produce people behaviour through algorithms. This is one primary reason why we are seeing more and more sophistication and complexity. As a result of this, we are able to find the connection in data which we weren't able to do in the past. When you are looking at credit, there is a great need for you to explain your decision and it took us a long time to include these complex models in credit. The adoption of explainable AI in credit has increased so much- that is we can give reasons based on outcomes of the models. Q. Which are some of the clients for Equifax India and could you point out a use case? There are many financial institutions and other enterprises that we are helping to the better credit decision. Now we are using AI and other deep learning technologies for what we call value-based pricing, that is pricing a service based on the data that we have. We are using AI to bring cost-efficiency and also apply it in all customer value chain- that is to identify targets of prospective customers, data collection, to identify people who haven't paid their bills or loan, for even decision making, cross-selling. One particular use case is that of ZoomCar wherein they wanted to improve the smooth running of their business. They approached us to know more about the customers' buying pattern. For instance, they wanted particular information about who wants to buy a car and who are the prospective customers and narrow it down to the selection of people and their identity based the data and financial information that we have. Q. What is Equifax India's future plan with regards to the adoption of emerging technologies like AI and ML? We are bringing new technology called Conrea to India, it is an analytical sound box where we have a lot of tools and capabilities in terms of analytics. The idea is to create a data lake, where were will push a lot of anonymised data with tools like Hadoop around it. Further, we plan to give access to this environment to universities and our other customers so that they can access data and try to find a new correlation or knowledge based on these data. So, our plan for 2019-2020 will bring this advanced environment to India with no limitation in terms of volume and processing.

Explainable AI Is Driving The Credit Analytics Market Now, Says Matthieu Garnier Of Equifax

Key takeaways

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