AI reveals patterns in product- and brand-level purchases to help banks improve marketing, grow deposits and make better lending decisions.
NEW YORK CITY, NY, UNITED STATES, September 15, 2026 /EINPresswire.com/ — Omnisient has launched Basket Signals in the U.S., enabling banks to work directly with retailers and apply AI to anonymized SKU-level shopping data – the specific products and brands consumers buy – to win new customers, grow deposits and improve lending decisions.
Basket Signals brings together retailer-direct product- and brand-level shopping data, AI and privacy-preserving collaboration, giving banks a way to learn from detailed purchase patterns while retailers retain control of their data. The product made its live debut at FinovateFall in New York on September 10.
What product choices can reveal and why banks care
Across billions of purchases, patterns can reveal changes in affordability, life stage and likely financial product needs. Basket Signals helps banks identify which patterns are most useful for finding prospects, improving marketing response and assessing repayment potential.
For banks, the opportunity is to focus acquisition spending on people more likely to qualify and respond, identify prospects for deposit products, and give potentially creditworthy applicants a second look based on patterns identified across billions of purchase decisions.
“See the signals in shopping baskets, without seeing who’s holding them,” said Jon Jacobson, CEO and Co-founder of Omnisient. “That’s the idea behind Basket Signals.”
For banks, that means learning from detailed shopping behavior while protecting the privacy of individual shoppers.
More precise marketing to win customers and grow deposits
Broad demographics alone offer limited insight into who needs a financial product or is likely to respond to an offer.
Basket Signals helps banks build more precise audiences using product- and brand-level shopping patterns. Banks can explore signals linked to likely product need and response, helping them focus campaigns on better-qualified prospects, including potential deposit customers.
Those audiences can be reached through participating retailers without the bank receiving individual shoppers’ identities or the retailer’s customer data.
In international deployments, this approach has reduced cost per applicant by 74% and delivered a 728% return on ad spend.
A second look for borrowers traditional data may miss
Consumers with thin, outdated or limited credit histories can be difficult for lenders to assess, even when they may be able to repay.
With the consumer’s consent, Basket Signals adds shopping-based signals to support a second look when conventional information is insufficient. The bank retains control of its credit policy, model governance and final lending decision.
Previous deployments have improved the ability to predict repayment by around 40% and helped 3.2 million consumers qualify for credit.
How banks learn without exchanging customer data
Banks and retailers collaborate inside Omnisient’s secure, neutral analytical environment. Banks work with anonymized profiles linked to product- and brand-level purchase behavior. Each party keeps control of its underlying data, and the bank does not receive identifiable shopping histories.
Built-in AI models and analytics tools allow banks to build, test and refine models for their own marketing, deposit growth and lending objectives.
Omnisient does not buy, sell or resell consumer shopping data.
Founded in 2019, Omnisient operates in the U.S., U.K., Middle East, Brazil and South Africa. Backed by TransUnion, both a strategic partner and minority investor, its technology is used by more than 100 enterprises and has protected more than 500 million consumer records.
Omnisient was named one of CNBC’s World’s Top Fintech Companies in 2026 and won Finovate’s 2024 Excellence in Financial Inclusion Award.
Julian Diaz
Omnisient
julian@omnisient.com
Visit us on social media:
LinkedIn
YouTube
Legal Disclaimer:
EIN Presswire provides this news content “as is” without warranty of any kind. We do not accept any responsibility or liability
for the accuracy, content, images, videos, licenses, completeness, legality, or reliability of the information contained in this
article. If you have any complaints or copyright issues related to this article, kindly contact the author above.
Media gallery
