ElectrifAi | Banking

Terms and Conditions

Use Cases That Make Us Different

Collections Management

Customers sometimes spend more than they can pay back. Banks then must send those accounts to collections in hopes of receiving some return. Machine learning can help optimize Collections and get the most return. Such as machine learning models that help with Collections risk that identify early, mid, and late-stage risk and the probability of receiving payment, then find the RPC (Right Party Contact) so the collection outbound call reaches the delinquent customer.

Credit Line Management

Banks can earn more revenue by increasing credit lines to encourage customers to spend more money with their credit card. To do so, banks periodically check a customer’s potential to increase their credit line but not take on risk if they cannot pay the bill. ElectrifAi’s machine learning solutions take guessing out of the equation and automate Credit Line Management.

Customer Experience

To remain prosperous, banks depend on customers keeping accounts long-term. The best way to mitigate churn is to increase the customer’s experience. ElectrifAi has many machine learning models that help with producing the ultimate customer experience, such as behavior scorecard, churn mitigation, customer intent, customer segmentation, demand forecasting, recommendation engine, and more.

Fraud Management

Fraud happens every day, whether intentional or accidental. Banks can plan for this risk through machine learning capabilities. Know if a client is likely to rack up credit charges with no intention of paying the bill, where fraud is likely to occur if a merchant has been compromised, or if a client accidentally defrauds the bank by incorrectly disputing a charge. Machine learning can process thousands of data points in the time it would take a person to process one account, thereby locating saving the bank even more in potential losses.

Marketing and Cross Sell

The most successful marketing campaigns are ones that appropriately target the right people. Machine learning analyzes purchasing behavior, payment history, demographics, etc., to send promotions and upsell opportunities to those most likely to be receptive to them. Machine learning can also predict those likely to opt-out from future promotions from email fatigue to prevent losing a customer. The promotions consider those who should not take on more credit by those with poor payment histories, reducing the bank from taking on additional risk.

Risk Management

Giving credit or promotions to a customer is a risky move for banks. Carefully balancing and managing that risk to not lose money but also give the customer the best rate possible is difficult to process manually. Machine learning makes taking risks easier by accurately pinpointing the exact risk level to take for each specific customer, increasing customer satisfaction while at the same time reducing the bank’s risk.

Spend and Contract Management

Efficient contract management can help banks reduce their spend. Whether contracts with individuals, businesses, or merchants, contracts can be tedious and confusing. Machine learning can help extract key terms to indicate when contracts expire, renew, or when they should be renegotiated. All this and more can be completed faster and with greater accuracy than a person can do on their own.