With personal finance, banking and the utilization of AI among the many topics that will be covered in depth at the upcoming DATAx New York festival, we spoke with Poulomi Damany, Vice President of Data Product Management at Credit Karma, to learn about how the company is using algorithms and new platforms to improve its predictive, data-driven recommendations.
We also asked Damany about her daily challenges and the creative ways in which Credit Karma utilizes analytics to improve its customer service and various products.
How does Credit Karma utilize AI processes to assist customers?
Poulomi Damany: AI systems consist of a reinforcing loop of learning from reasoning to self-correction. At Credit Karma, we have more than 80 million members who visit our platform regularly to review their financial profile, enabling us to learn at scale. We collect close to nine terabytes of data per day and use these observations to inform our predictions of things happening in the not-so-distant future.
For example, we know that 80% of our members visit Credit Karma within 90 days of a mortgage tradeline appearing on their credit report. With that knowledge, we’re able to surface recommendations for mortgages relevant to them and that they are likely to be approved for. We run 8 billion such predictions every day and use the results to tune and improve our recommendations for our members.
To find out more about how AI and machine learning technologies are transforming the banking industry, visit the AI & Big Data for Banking Summit, part of DATAx New York, on December 12–13, 2018
How has blockchain technology transformed the financial services industry?
Damany: Blockchain, a technology which continues to grow exponentially, is yet another example of the digitization of the financial services industry. While blockchain doesn’t directly impact our core business at this time, it could be viewed as a necessary step toward revolutionizing the banking and financial services industries. Only time will tell.
How do you leverage AI and machine learning to make business decisions?
Damany: We use deep-learning models to provide personalized recommendations to our members. We also leverage similar techniques to inform our business decisions. For example, we can better identify new macro trends and abnormalities in our business metrics, which we use to improve our business forecasts. Similarly, our marketing teams use predictive analytics to create optimal bids for our digital advertising campaigns.
What disruptions do you foresee shaking up your industry in the next year?
Damany: From an industry perspective, we’re seeing central bank rate increases, driving up interest income for financial institutions. This could lead to a rise in defaults and loan losses, increasing the cost of credit and offsetting improvements in net interest income.
Given that, there’s an even greater need for technological solutions that address consumer pain points and assist them in making more informed decisions to make financial progress. This is particularly true for consumers who are new to credit or trying to repair their credit, like millennials for example.
Nearly half of all US millennials are Credit Karma members and we are leaning into delivering solutions they are most comfortable consuming. This includes mobile-first services for comparing and applying for financial products, intelligent chatbots providing actionable financial advice and saving information in mobile wallets for ease of use.
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