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Artificial intelligence is transforming industries across the globe, and personal finance is no exception. The sheer scale of financial data – from one billion daily credit card transactions to the 130 million Americans with personal loans – requires powerful predictive capabilities for financial service providers to find true signals in such noisy data. Predictive machine learning allows businesses to deliver best-in-class services and deepen customer relationships by understanding their clients’ financial lives and anticipating their needs.

Consumers have more options than ever to manage their finances, so creating a seamless digital experience that’s both highly personalized and fully compliant becomes an essential competitive advantage. Using predictive machine learning is also critical for identifying the right customers to target or determining the key drivers to improve engagement and retention. This blog will dive into different ways personal finance companies can leverage predictive ML to improve operations and delight customers.

Identifying and prioritizing the most valuable customers

Financial service providers have limited resources and personnel to dedicate to their customers, so identifying the highest quality potential customers are essential for improving long-term profitability. Understanding what the likely spend and purchasing behavior of consumers are allows you to prioritize outreach efforts and marketing campaigns to onboard and enable these high spending customers. Predicting which individuals have the potential to become a “quality customer” is best accomplished using sophisticated machine learning that takes into account unique customer characteristics and makes connections between these characteristics and past behavior to determine what their future behavior will be. Another way to determine a quality customer is by analyzing default risk and identifying which users are likely candidates for churning or not paying. A good example is Affirm - the largest buy-now-pay-later loan provider in the U.S. built a successful business by leveraging machine learning to predict loan default rates across its user base.

Recommending Personalized Products and Services

Once a business understands customer quality, the next big challenge is matching customers with the right products. Financial advising and wealth management is an area that requires a highly personalized approach in order to be effective. Delivering a highly personalized financial plan is often seen as essential by the consumer (whether for savings, taxes, insurance coverage) but this requires a deep understanding of each individual’s unique circumstances, goals, and preferences. Doing this effectively is very time-consuming and doesn’t scale, so advisors are limited in the number of clients they can take on. On the other hand, traditional platforms that automate recommendations are limited in their effectiveness if they are unable to incorporate that nuanced understanding of their users. Recommendation challenges are not limited to financial advising and other personal finance services face similar questions.

These types of problems are well-suited for predictive machine learning to understand interconnected data types to give a view of a customer’s financial situation.

Morgan Stanley applied machine learning to recommend effective investment strategies to its wealth management clients. The bank credits their machine learning models with unlocking a deeper understanding of their clients needs. So far, the predictive models have helped financial advisors tailor investment options for their clients and provide operational alerts when market changes or a client’s life event encourage changes to their financial plan.

Identifying Cross-Selling and Upselling Opportunities

Personal finance platforms drive much of their revenue by providing complementary services to existing high-value customers. Yet in today’s financial landscape, customers pick and choose from product offerings across competitors, and it can be difficult for businesses to sell their full range of products to a client. That becomes an issue for retail banks and other personal finance platforms because selling products to existing customers is much easier than bringing in new leads.

Machine learning offers a chance to dramatically improve a business’ cross-selling and upselling potential. Personal finance businesses often have a lot of data about their customers, but many do not capture the value of that data through predictive analytics.

Visa credits their machine learning capabilities for unlocking this use case for banking clients. The credit card provider predicts a cardholder’s future behavior such as upcoming travel allowing the bank’s clients to market relevant travel-related offers. Over 50 banks that use the solution see an average of 14.5% increase in cross-border travel spend per credit card.

Fraud Detection and Prevention

Credit card providers and payment processors face major fraud issues, including over $30 billion in payment fraud each year. One study found that for each $1 of fraud in their products, businesses had to spend $3.75 in additional remediation costs.

Despite the costs, detecting fraud remains a persistent problem for credit and payment providers. There are far more legitimate than fraudulent transactions in any payment network, and the data imbalance turns the detection problem into picking a needle out of a haystack. Fraudsters also continue to evolve their methods to avoid detection, meaning that businesses need to constantly evolve their detection systems to keep up.

Traditional ML approaches to fight fraud have severe limitations, so companies that stick to rule-based techniques fall behind as fraud evolves in real-time. That is why advanced ML approaches have become more popular for consumer financial products. American Express credited new machine learning techniques for dramatically reducing account takeovers, and the company boasts the lowest U.S. fraud rates among major credit card networks. For American Express, lower fraud rates translate into greater revenues for merchants and reduced custom frustration with the transaction experience. Better yet, their ML systems achieve this without sacrificing transaction processing speed.

Specifically, graph learning has emerged as one the most effective ways of combating fraud and abuse in financial networks. Paypal now uses graph-based unsupervised learning to fight fraud and “almost all of Paypal risk models are using graph features.”

Kumo: A Single Platform for all Consumer Finance Applications

Building AI capabilities for financial products is a major investment for companies. They need to set up dedicated infrastructure, bespoke data pipelines, production tooling, and many prediction models for each use case. These efforts often require large teams of skilled data and ML practitioners. What’s more, consumer finance often mandates considerable data governance and security measures to meet regulatory requirements.

Kumo brings a new approach that leverages representational learning on relational data, enabling you to perform state-of-the-art machine learning directly on raw data. Analysts, app developers, and business owners can define ML models directly with Kumo’s no code platform, while machine learning practitioners can improve existing models and tackle new use cases. What’s more, the graph learning approach is ideally suited to handle the relational data that captures users and their behaviors in financial networks.

Kumo allows you to build a 360 understanding of your users and generate predictions quickly with no additional overhead - whether it’s personalizing offerings and services based on unique preferences, quantifying future consumer spend to identify how to prioritize resources, identifying users who are likely to churn or reduce activity, or capturing anomalous behavior. Kumo’s detailed feature-level explainability metrics provides visibility into drivers of user behavior. All of this is done securely and reliably, enabling a fully compliant environment.

Personal finance services sit on troves of data, but many still don’t have the capabilities to take advantage of those resources. Kumo offers a shortcut to power products with best-in-class predictions regardless of ML experience.

If you’d like to try it out, request a demo today!

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