The next thing is automating customer profiling and relative spending ranking,” said Kumar. The AI metrics include: accuracy (cash flow) completeness (recurrent transaction discovery) precision, recall and AUC like credit default and anomaly detection cross-entropy (categorisation) custom (MCMC) for optimisation for savings AIC, KL-divergence (particularly clustering) etc. The essential business metrics include the voice of the customer (VoC), retention, click-through rate (CTR), net promoter score (NPS), profit and loss (PnL), relative rankings, and other capabilities with respect to competition. In addition, the company leverages Python, Pyspark, R, SQL, and SWIFT (OS) in terms of programming languages. For data storage, Intuit Mint currently uses Hive, DynamoDB, Redshift, S3. Further, they used EC2 inference nodes for real-time scoring (tens of millions of transactions scored per day), along with EMR or clusters of nodes for better processing (train up to 100s and millions of ML models within a few hours or several x 1000 core clusters), on-device or federated learning (iOS), etc. “We had to deal with a lot of legacy systems, which we migrated over to AWS,” said Kumar. Kumar explained the strong and growing competition in the space, coupled with challenges like data completeness, dynamic nature of transactions data, user fatigue, and legacy systems. Making decisions and predicting outcomes using these data is easier said than done.

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