Segment customers<\/h2>\n\n\n\n
By using big data, banks can segment customers with more granularity than ever before. Instead of grouping customers into broad categories like by age or income, banks can segment their customer base into more targeted groups. These groups can include basic demographics plus information about customer behavior. Customer behavior could include spending habits, relationships with other bank customers, and what products or offers they\u2019ve accepted before.<\/p>\n\n\n\n
This kind of data-driven customer segmentation can help banks better target marketing toward customers and support them where needed. With the help of customer data, banks are even able to target customers down to the individual. We\u2019ll speak more on customer personalization a little later on in this blog.<\/p>\n\n\n\n
Assess Creditworthiness<\/h2>\n\n\n\n
One way banks can leverage customer data is to paint a more accurate picture of a customer\u2019s creditworthiness. Traditionally, creditworthiness in the U.S. is acquired through one of a few credit bureaus. These institutions determine a customer\u2019s credit score using basic indicators. Common indicators include the number of existing loans and credit cards, loan payment history, credit utilization, average account age, and credit inquiries.<\/p>\n\n\n\n
But in reality, there is so much more that goes into a person\u2019s actual creditworthiness. Most importantly, a credit score does not take into account income or transactional data. These two factors are critical to a person\u2019s creditworthiness. That\u2019s because they indicate how much capacity a person has to spend (and pay off) and how much a person actually spends.<\/p>\n\n\n\n
Let\u2019s take a look at income first. The digital revolution has given rise to new ways of earning money, making our understanding of a person\u2019s income more complex than it used to be. Online shops, drop shipping, food delivery, ridesharing apps, freelance work, and more have become popular ways to diversify income. In fact, more than a third of Americans<\/a> participate in the gig economy. Data analysis can help banks make sense of inconsistent income streams to get a more accurate picture of a customer\u2019s creditworthiness.<\/p>\n\n\n\n Transactional data, on the other hand, can help banks determine how much a person is spending and how they\u2019re spending. These transactions can impact a customer\u2019s ability to pay back loans and complete credit card payments. A few things to look for in transactional data include buy now pay later accounts, ongoing payments like utility bills or insurance payments, and cryptocurrency investments.<\/p>\n\n\n\n Financial fraud is nothing new, but the rise of online banking has given rise to new fraud methods. In fact, consumers lost a whopping $5.8 billion to fraud in 2021 according to the FTC.<\/p>\n\n\n\n Instead of fraudulently accessing funds from a human teller, bad actors have come up with creative ways to compromise customer identity and access online bank accounts. They can use stolen credentials, phishing, and stolen credit card information to make purchases and access accounts.<\/p>\n\n\n\n Luckily, advanced analytics can help banks predict and detect fraud more easily, making banking safer for consumers. Machine learning algorithms can help banks identify both individual and industry-wide trends in consumer data and spot outliers. This can hopefully catch fraud before it happens or before a customer notices.<\/p>\n\n\n\n A method called fraud risk analytics uses machine learning to apply a risk assessment score to consumer transactions in real time. Depending on the score applied, the algorithm can recommend how to move forward, whether that be accepting or denying the transaction or requiring further authentication.<\/p>\n\n\nDecrease Fraud<\/h2>\n\n\n\n
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