766 deep dives in 24 categories · last updated
27 matching deep dives
Research Lab: these deep dives are AI working drafts unless tagged Reviewed research. Read how review works.
Financial AI, data and technology, including tools, operating benefits and their limitations.
Spring Labs sells AI software that reviews bank customers' calls, emails and chats to find complaints and check service quality. On October 7, 2026, it acquired Winnow and launched SpringGRC, a compliance-management platform now in limited beta.
Affirm reported more completed purchases after adding transformer-derived credit information to its underwriting. The result concerns a defined experiment; repayment, explanations and the economics of those extra purchases remain separate parts of the evidence.
Alloy connects identity data, fraud scores and review tools. Its customer examples show how that can change account opening, while leaving important questions about losses, cost and customer experience.
AWS offers configurable filters around AI interactions. The important boundaries are which content reaches them, what a grounding score means and how many legitimate requests are blocked alongside harmful ones.
Fannie Mae’s validation service can reduce paperwork and defined loan-sale exposure. Its latest employment-verification update shows why the evidence, closing date and scope of contractual relief still matter.
BioCatch analyzes how people interact with financial services. Visa’s announced acquisition highlights the technology’s role, but behavioral signals, research prototypes and proven customer outcomes remain different things.
Visa completed its purchase of Featurespace in 2024. A NatWest case shows how behavioral analytics can influence payment decisions, and why detected fraud value is different from losses prevented.
Fiddler describes tools for observing predictive models, tracing AI agents and filtering requests. The distinction between an early warning, a confirmed error and a successful correction explains both their usefulness and their limits.
VantageScore 4.0 is available for eligible Enterprise loans and Fannie Mae has aligned pricing with Classic FICO. FICO 10T remains a future delivery option. Model choice, report coverage and the meaning of a cutoff are separate parts of the transition.
NICE Actimize combines rules, customer comparisons and alert ranking in its monitoring products. Following an alert into an investigation shows why fewer alerts can coexist with unchanged work or weaker detection.
Cash Atlas turns permissioned account data into reports and attributes; NovaScore adds a proprietary risk score. Customer announcements show where those tools are being applied, while seasonal income and connection costs explain the limits of a richer data feed.
Provenir combines data, rules, models and AI-assisted workflows across the customer relationship. Its broad product claims become meaningful only when the task, customer experience and economic comparison are kept distinct.
Entity resolution brings scattered records together; network analysis shows their relationships. Useful context depends on keeping unrelated people and businesses apart.
SAS links model deployment with records of ownership, validation and permitted use. The distinction matters when a technically successful release delivers the wrong version, stale data or an answer that arrives too late.
SentiLink’s materials distinguish identity verification, fraud signals and credit evaluation. A complete result also depends on false positives, usable explanations and a way to resolve mistakes.
Socure’s synthetic-identity tools combine scores, reason codes and verification options. Their value depends on what happens after an identity is flagged, and on keeping a relative risk ranking distinct from proof of fraud.
Taktile combines rules, data, case work and AI agents. Its financial-spreading benchmark shows one kind of measured result; completed applications, consequential errors and time released tell different parts of the story.
ThetaRay describes monitoring that uses behavioral and network patterns to identify unusual activity. A payment example shows the difference between detecting a change, establishing suspicion and completing an investigation.
DataRobot’s monitoring tools show how deployed AI models and agents behave. Their value depends on connecting those records to what happened to the customer and the work needed to resolve a problem.
Agricultural loans and Brazilian payroll lending show how FICO Platform connects information, models and lending rules. Faster processing is a different result from safer loans or cash savings.
Plaid turns permissioned bank-account activity into reports, income estimates and credit-risk scores. Its newer tools change access and modelling, while leaving the lender responsible for the final answer.
Stratyfy helps lenders turn historical data and staff judgement into lending rules. A community-lender programme offers concrete examples, with important limits on what its reported gains prove.
Zest AI builds machine-learning underwriting for lenders. Idaho Central and First Hawaiian show how automation can change an application, while reported decision rates leave repayment and funding questions open.
Mesh connects financial-crime screening with case-management tools. A possible match can prompt further checks, but it does not by itself establish wrongdoing.
Wio Bank and Jack Henry show how Feedzai’s technology reaches financial institutions, and why product adoption alone does not measure fraud prevention or customer disruption.
PowerCurve turns data, predictive models and a lender’s policies into decisions and customer workflows. Its separate products, real deployments and place within Experian’s Ascend Platform explain what the software does, and what its performance claims cannot establish.
How documented monitoring and AI-assisted functions fit payment and onboarding workflows, with a focus on completed work, service quality and evidence limits.
Page 1 of 1 · 1–27 of 27 articles