AI & ML Credit

Experian Sharpens Focus On Developing A Game-Changing Offering Using Analytics And AI (Peter High, Forbes)

Shri Santhanam came to the world of AI and analytics first in serving clients as a consultant at Mercer and Oliver Wyman. Next, he co-founded OW Labs to make sense of client data, unearth hidden knowledge, surface insights where clients need them, and reinvent decision making, building up a team of roughly 100 in the process.

In June of 2019, Santhanam moved over to nearly $5 billion revenue Experian, a leader in consumer credit reporting company. The company has always been a data company, but Santhanam’s arrival underscores an increased focus on the use of analytics and artificial intelligence to benefit customers. The goal of his group is to introduce game-changing products and services.

(To listen to an unabridged podcast version of this interview, please click this link. To read future stories like this one, please follow me on Twitter @PeterAHigh.)Today In: CIO Network

Peter High: You are the Executive Vice President and General Manager of Global Analytics and Artificial Intelligence [AI] at Experian. This role was created especially for you when you joined in June 2019. Describe the different aspects of your role. 

Shri Santhanam: I am responsible for the Global Analytics and AI business. The objective is simple. It is to accelerate the impact that we can have on businesses, consumers, and economies through analytics and AI. Historically, Experian has been a data company. We have leveraged the power of data to bring a transformative impact to consumers in economies for over 100 years. The executive team at Experian sees enormous potential in both analytics and AI to amplify the impact that we have. My job is to fundamentally accelerate that.


In the six months I have been here, I have seen excellence across three different areas. I have seen some of the most valuable data sets on the planet, which Experian has. I have seen some of the industry-leading tech capabilities we have. In our data labs, which we have in North America, UK, South America, and fairly recently opened in Asia Pacific, I have seen world-class analytics and AI talent.

The big opportunity for us, and it is also a challenge going forward, is how we bring those three together, the data, the tech and the analytics to produce the game-changing products and services that AI can bring to our customers. 

We are starting on that journey already and, my charter has fundamentally accelerated and supercharged that, allowing us to fully unleash our potential.

High: As you mentioned, you are relatively early in your tenure within the organization. How do you see this impacting operations inside the enterprise but also on behalf of the company’s customers? 

Santhanam:  A good example of what Experian has already kicked off and of what we have done to drive impact with consumers is Experian Boost. This is an example of how we have brought data, technology and analytics to truly produce transformative impact to individual consumers, but also to support businesses. If you look at traditional tradelines or information on credit you have for consumers, they do not cover utility bills or cellphone bills, and the payment activity you have on those carries a valuable signal on your ability to repay.

We have created a product where you can voluntarily sign up, offer that information, include that in your tradeline and profile, and boost your score. It has been extremely successful. That is a great example of what we want to do more of and at a scale across a number of our businesses. Another example is our work to bring to market a product we call the Ascend Analytical Sandbox.

One big challenge the financial services community and other industries face is creating a scalable analytical environment that is capable of housing enormous amounts of data and carrying out powerful AI and analytics. If you go to Google and Amazon, it is bread and butter for them, but having an environment where you have data that is securely stored with scalable computational capacity, capable of carrying out powerful AI and analytics, that is hard to create. We have created this in a product called Ascend and it is being offered to all of our clients across the board.

It is an example of how we are furthering our mission to fundamentally democratize analytics and AI. Those are two examples where we expect to do much more to bring the transformative impact that we see is necessary for our customers, consumers, and clients.

High: How do you engage with the rest of Experian? Is yours a role that is collaborative across the organization, drawing from the various technology teams, leveraging data sets that pre-existed and so forth? What is the dynamic of broader collaboration within the company?

Santhanam: The charter for my team is twofold. 

1. Amplify the impacts of analytics and AI. Take the existing footprint of analytics. That includes scores, and we have a suite of products around decisioning in fraud and IT and within that business, amplifying the impact of analytics and AI which already exist. That is a natural and a structural part of what my team is already set up to do and there is a set of interactions with the existing team which allows for that.

2. Powering the rest of Experian. Today, there is an enormous belief and a desire to bring analytics and AI to scale across a number of our other business units, such as healthcare, auto, and our entire consumer data business. My role is to find the right way to create this horizontal capability, which allows us to democratize analytics and AI and power the rest of Experian. Ultimately, we have a vision where we see analytics and AI being much more accessible.

What we intend to do is change that. Through the concept of a citizen data scientist, we intend to allow for a much larger number of folks who are domain experts in these other business units to be able to drive and leverage analytics and AI to bring to market products and services which embed those capabilities much faster.

High: How have you scaled up? Do you have a team around you? Are you in the process of drawing talent from across Experian in combination with bringing in new people who already have relevant backgrounds?

Santhanam: My team is a combination of three constituents of people. 

1. Domain Experts. These are folks in the business who know the various industry verticals and have a deep understanding of the most important customer problems that we need to solve. 

2. Outsiders who bring in a completely different perspective. Some of them are from Silicon Valley. Experian has a precedent of building some of those capabilities already. Bringing in folks who [offer] a perspective different from what we have historically seen in the area of AI, analytics and machine learning (ML), who do not necessarily have the domain expertise but have to learn. 

3. The most important category is people that are cross-functional experts. These are people who can think across the commercial domain, across technology and across data science, who can bring that entirety together and help articulate and develop these products and services and create the vision of these products and services.

It is those three types of folks that we are in the process of bringing on board. It is still early days, but we are finding a lot of support across the business to do that and there is a belief that there is value that will come from bringing those three constituents of people together.

High: Prior to Experian, you spent 16 years at two consultancies that are now part of the Marsh & McLennan: Mercer and Oliver Wyman. You were a senior partner and co-founder of Oliver Wyman Labs [OW Labs]. Could you talk about your experience as a consultant and how having a toolkit of client oriented problem solving in a diverse array of industries has helped you now in your current role at Experian?

Santhanam: Half that time was as a traditional consultant and I was focused on strategic and operational problems across a set of industries for the first seven or eight years. In the last six or seven years, my focus was around founding, co-founding and building out Oliver Wyman Labs. This had some of the aspects of traditional consulting which included strategy and operations, but a big part of it was bringing Silicon Valley-style AI, analytics and tech to large corporations. The objective was to unleash the potential of that capability within large corporations and to mirror that image and bring Google and Amazon-style tech and analytics capability.

There is an enormous opportunity in this space. We are all keenly aware of the revolution around data and computing, and the price of both storage and computing has dropped significantly. The other area that is coming to the forefront now is open source. Google and other major tech companies are making a lot of that technology. The recipes that they are creating and using are available for the broader community to utilize and to apply against problems. We saw that as an enormous opportunity at OW Labs. 

It is difficult for a large company to be set up to take and leverage either a Google TensorFlow library or to look at their frameworks and drive impact from that. At OW Labs, I brought that capability around tech, analytics and AI to large companies and problems. There was an opportunity to further increase, impact and drive scale by combining what we were doing with data assets to start creating products and building services.

My experience at OW labs is the reason for my belief in the enormous potential that Experian has. At Experian, there is a powerful combination of assets around data, technology, and analytics that can be brought together to build products and services which are scalable to go after that same opportunity. I decided to make the transition to Experian because of my belief in that opportunity and the assets and the capabilities that Experian has in this space.

High: As you were developing this part of the business, are there certain sectors or certain industries that are particularly important targets for you? Or is it a broader approach that you are taking?

Santhanam: Financial services are an important space simply because Experian has a significant set of relationships with many global financial institutions. In the medium to longer-term, the greater opportunity is likely outside of financial services. A recent study from McKinsey posts that there is an opportunity of more than five trillion dollars. The majority of that opportunity is outside of the traditional technology sectors and financial services. The opportunity all around analytics and AI are certainly beyond the verticals of financial services which we currently operate in.

High: This is a growing and increasingly important space that is on the minds of a great number of organizations across industries: the unfortunate paucity of resources. People with the appropriate skills seem to go to a smaller number of leading companies in this space, such as traditional Silicon Valley behemoths.

A lot of large organizations have had to take an ecosystem approach in building out capabilities concerning artificial intelligence. As somebody who has been part of that ecosystem and who has hired into your own organizations, can you talk about the talent necessary to fulfill all you have described? 

Santhanam: It is an acute problem which several companies are facing. Andrew Ng recently shed some data on the demand for data scientists and the job openings that Coursera is seeing, and the numbers are staggering. There is a five to tenfold increase in the demand and job openings for machine learning, data science and analytics-capable individuals. It is a big problem in the industry. It is an issue that we are focusing on to ensure that we have the right channels into the talent.

What is likely to happen over the next four or five years is that the effort to democratize a number of these technologies is going to make a significant amount of progress. In the last year or two, tools, training and capabilities have been created that allow for domain experts that do not have math or computer science expertise to dive into the algorithms of data science.

The approach we intend to take is twofold. We intend to create an organizational model, an ecosystem approach, which allows us to attract that talent and we intend to make investments to democratize that capability. You do not necessarily need to have everyone be at that level of data science competence to operate effectively.

High: As you look to the future, are there other trends that particularly excite you that are making their way onto either your personal or professional roadmaps?

Santhanam: In this past year, we have seen several exciting developments in the analytics and AI space. Going forward, there are two areas in my mind which stand out. 

1. AI trust and explainability. 

There has been so much focus on getting analytics and AI to outperform traditional methods but now there is a need to expand and allow for all of that success to be explained. There are challenges even with companies such as Amazon, IBM and Microsoft, where Microsoft put out a chatbot that was corrupted by Twitter trolls. Amazon has had challenges in recruiting concerning issues of bias and fairness. In the coming year, there is going to be a lot more focus on trust. 

2. Data science and machine learning will start to become more of an engineering discipline.

Traditionally data science has been more a bespoke exercise, and there is a lot that falls in the gap between a data scientist and the technologist. Companies such as Google are making a lot of progress in this space and are making that knowledge available for the broader community to use. Machine learning operations is an area where the whole notion of bringing in disciplines from software development into data science is starting to come to the forefront. That is another area where we are going to see a lot of activity.

These two areas are important as we think about our vision going forward in driving impact with our customers and consumers. Being able to operate at scale with AI and ML, and being able to deliver it in a way that is safe and trustworthy is key to us.

Peter High is President of  Metis Strategy , a business and IT advisory firm. His latest book is  Implementing World Class IT Strategy . He is also the author of  World Class IT: Why Businesses Succeed When IT Triumphs .  Peter moderates the Technovation podcast series. He speaks at conferences around the world. Follow him on Twitter @PeterAHigh.

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