Personalizing Financial Products Used to Be Hard, But not Anymore

  • Reesha Dedhia
    Reesha Dedhia
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Personalization can be a powerful tool for reaching and converting new customers–and better serving the customers you already have–by delivering a message that is in-tune with their interests and needs. According to a study by Forrester, AI-driven personalized marketing was responsible for an average 5% incremental improvement in customer conversions and a 5.5% increase in online purchases. That's impressive; but what if AI could be used to personalize products as well as messaging?

The financial services industry is grappling with that very issue. On January 18, 2021, reported on a study by the Digital Banking Report, "Lack of Personalization Puts Banks at Odds with Customer Expectations," showing that, while consumers have grown used to the idea that organizations can cater to their needs and preferences, "75% of financial institutions considered themselves inept at determining the next best product offering on an individual basis." What's more, the study found that a majority of financial institutions have not prioritized financial product personalization.

Financial Services Personalization is Hard

The reason is that personalization in financial services needs a lot of data, and relies heavily on the kind of data that financial services organizations are required to keep secure. That puts financial services organizations in a dilemma. According to the report, the four biggest challenges to starting on the path to personalization are:

  • A large majority of financial services organizations said the collection and management of data is their biggest challenge;
  • Many financial services organizations lack internal and external expertise in data science;
  • Many financial services institutions said their organizations are hindered by siloed data, preventing them from operationalizing data in a timely manner; and,
  • Most financial services organizations do not have the *tools and technology *required to effect personalization across their customer base. These results are not surprising. Consumer financial data is one of the most sensitive forms of personally identifiable information (PII). From the moment an organization collects an individual's account or transaction information, it must secure that data–and keep it secured–or risk a data breach. The problem is, traditional approaches to data modeling require that data be decrypted in order to be mined for the valuable insights and predictions that can be extracted from detailed customer records.

The Risks of Unsecured Financial Data

Decryption puts data at risk, and those risks are unacceptable to reputable financial services organizations because a data security breach is a costly prospect. According to a recent cost of a data breach report, the average cost of a data breach for financial services organizations is $5.72 million, including legal fees, fines, remediation services, loss of brand trust, and customer defections.

But the bigger risk may be in the lost opportunities, because while traditional financial services organizations are figuring out how to get started down the path to personalization, non-traditional players have already begun. This point was made in the Bank for International Settlements (BIS) July 2021 report, Fintech and the digital transformation of financial services: implications for market structure and public policy, which observed: "Consumer interfaces are changing from physical branches to convenient digital access from anywhere. Digital consumer interfaces (e.g. web, mobile) enable new players to directly reach consumers both near and far. By using apps from different competitors, consumers can build their own complete bank and gain more personalized services. This has likely reduced the value of legacy physical networks and the incumbent's grip on the customer, who can easily shift funds to different providers to access preferred services."

Solving the Personalization Puzzle for Financial Services

How can traditional financial services organizations respond to the competitive influx from innovative fintech companies and non-traditional players in financial services? One thing that the incumbent institutions have that newer entrants to the market lack is huge stores of rich customer data that can be used to run powerful models that can deliver business intelligence and predictions. These insights are key to transforming products and personalizing products and services–not merely messaging–to catch up to, and even surpass, customer expectations.

The question, therefore, is: how can financial services organizations operationalize their data stores while still keeping them secure?

Often, when sensitive data is collected, it is encrypted and moved to on-premises or cloud-based storage, where it sits unused. About half of all data is currently stored in the cloud, and the popularity of cloud storage services like Snowflake have that number trending up. However, data must be operationalized if it is to have real value. A recent IDC report suggests that 68% of all data collected by organizations goes unleveraged because of security concerns and the difficulties associated with formatting. Traditionally it has been hard to extract business intelligence from encrypted data, and any time data is unencrypted it is at risk, and so it is secured, stored, and ignored.

Safely Operationalize Data in the Snowflake Financial Services Data Cloud

Our partnership with Snowflake is important for financial services organizations. The risks and difficulties of using encrypted data in the cloud have been removed from the equation with Cape Privacy's powerful combination of secure multiparty computation and secret sharing. Now, financial services organizations can collect and encrypt data, move it to Snowflake, and operationalize that data without ever having to decrypt it.

By running powerful AI predictions on rich consumer data within Snowflake, financial services organizations can unlock insights vital to delivering the kinds of personalized financial products consumers want and expect. And because AI prediction models feed off of rich data resources, many legacy financial services organizations have the means to gain a more precise understanding of their customers.

Data encrypted at the point of capture, and encrypted historical data that has gone unused, can be operationalized without ever having to decrypt it, ensuring its security throughout the entire process. That means complete records can be used, rather than data that is tokenized or anonymized and scrubbed of its most sensitive elements. AI models run on rich, complete data sources produce better results.

With Cape Privacy, financial services firms in the Snowflake data cloud can put their high-value, sensitive financial data assets to work at closing the gap between themselves and the new generation of fintech innovators, and use their data assets to meet customer expectations for personalized financial products and services. To learn more about Cape Privacy and our platform, visit our website. Or contact Cape Privacy for more information.

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