Results: A feature platform to streamline the development and deployment of real-time ML use cases
With Tecton, Tide has not only cut the time it takes to deploy a model from 2–4 months to just 1 month, they also have improved model accuracy with 7x more features and now deploy 2x more models than they had previously. They’ve also alleviated pressure on engineering hiring and project management and made high-quality feature engineering core to the company’s ML-driven product strategy to automate as many processes as possible.
With the help of Tecton’s feature platform for real-time ML, Tide has successfully created a set of tools and solutions designed to improve customer experience.
Tide and Tecton, in practice
An example of how Tide uses Tecton in practice is its automated risk analytics data product. To ensure Tide customers can safely open business current accounts straight from their mobile phone by scanning a photo ID, Tide must be able to approve new clients at the time of their request. To minimize friction for customer onboarding, reduce the number of hours spent on manual reviews, and speed up new account approvals, Tide set out to automate the process with immediate, real-time predictions of credit risk per customer.
#1: Risk Analytics: Automated Credit Risk Assessment for New Account Approvals
Challenge: Batch and Manual Credit Risk Detection
SMBs apply for business accounts or loans and expect a decision from Tide within seconds. During this new customer approval and onboarding process, Tide must simultaneously evaluate risk while providing a smooth customer experience. Tide must tread carefully when approving such customer requests because, for example, if a loan is approved but later defaults, Tide accrues financial losses. On the other hand, rejecting loans for qualified applicants is a bad customer experience, decreases conversion rates, and increases acquisition costs.
Tide’s risk evaluation must be highly accurate to avoid future loan defaults and must provide results in under 200 milliseconds so that customers do not experience delays during the sign-up process. For this to happen, Tide faced the challenge of serving features online based on streaming data— their previous models used only batch data and offline features.
Solution: Real-Time Risk Assessment
Tide built models to detect different types of risk across the hundreds of thousands of transactions that occur daily on their platform. These models leverage fresh features from transactional information, behavioral data, third-party data (e.g., credit bureau scores), and streaming data to accurately assess credit risk for individual accounts in real-time. Tide automated the use of high-quality features like transaction history or features related to not fulfilling government obligations to compute risk scores for individual customers. They found that their credit models were more impacted by transaction history from longer time periods than fraud models that benefited more from the short-term transaction history.
Result: $600k Cost Savings
By implementing high-quality features in their credit risk assessment models, Tide has increased approval rates by 50% and decreased the average loss per credit underwritten by 5%. Furthermore, the company has reduced spending on manual reviews for new accounts by over $600K/year.
#2: Real-time Transaction Fraud Detection
Another example of a finance tool Tide built and deployed with Tecton is real-time fraud detection which thousands of SMBs depend on to safely process transactions.
Challenge: Failure to detect fraudulent transactions leads to poor customer experiences
To build customer trust and create a safer user environment, Tide needed to improve a fraud detection solution that could continuously profile and identify risk across user sessions at the exact moment behavior potentially indicated fraud without increasing false positives or negatively impacting user experience.
Solution: Online inference powered by fresh features from real-time data
Tide uses transactional data, behavioral data, and third-party data (e.g., credit bureau scores) to learn from past fraudulent behavior in order to predict, in seconds, the likelihood that a new transaction is fraudulent. As part of Tide’s unified machine learning data layer, Tecton allows teams to orchestrate and leverage historical and real-time features for both training and backtesting, as well as powering models with real-time and batch source feature pipelines for real-time inference.
To build their fraud detection system, Tide automated the use of high-quality features like transaction history or features related to not fulfilling government obligations. They found that transaction fraud models benefit more from short-term transaction history than credit models, which are more impacted by transaction history over longer time periods.
Results: Tide significantly reduced false positives and improved real-time fraud detection
By implementing high-quality features in their fraud risk assessment models, Tide has decreased blocked transactions by 20% all the while seeing a 4x increase in fraud likelihood for every blocked transaction.
