Informative Research vs Point Predictive

What the rubric says

Informative Research and Point Predictive are both scored in Verification & Data. Informative Research carries an overall of 4.3, Point Predictive an overall of 2.1. The widest gap between them is Integrations and ecosystem, at 3.3 of a point. That axis measures how well it reaches the rest of the stack. Informative Research takes it, 4.9 to 1.6.

Where the five axes separate

On Integrations and ecosystem the record favours Informative Research, 4.9 against 1.6. On Functionality and depth the record favours Informative Research, 4.5 against 2.3. On Production impact the record favours Informative Research, 4.4 against 2.3. On Adoption and support the record favours Informative Research, 4 against 2.1. On Return on spend the record favours Informative Research, 3.8 against 2.1.

Pricing posture

Informative Research does not publish pricing. Its listed model is quote only, with credit bureau costs passed through. Point Predictive does not publish pricing. Its listed model is quote only.

Deployment and who each one targets

Deployment for Informative Research: Cloud, with LOS and point of sale integrations. Deployment for Point Predictive: Cloud, delivery mechanism not published. Segment focus for Informative Research: Mortgage lenders and independent mortgage banks consolidating credit and verification onto one supplier. Segment focus for Point Predictive: Higher-volume lenders using fraud scores to triage underwriting, with auto lending as the company’s core. The two entries name different buyers.

What each record credits

Informative Research: AccountChek supports Day 1 Certainty and Freddie Mac’s asset and income modeler. Informative Research: Names a dozen integrations, from Encompass and MeridianLink to nCino and Floify. Informative Research: Credit, assets, income and employment return on one order, not separate transactions. Point Predictive: MortgagePass rank-orders applications by fraud propensity, enabling risk-based triage over binary flags. Point Predictive: Draws on application, servicing, real estate and behavioural data plus its consortium repository. Point Predictive: Published paystub fraud research, finding one in ten submitted paystubs fake, grounds the modelling.

What each record holds against them

Informative Research: Tri-merge costs are bureau pass-throughs, outside the vendor’s control. Informative Research: No pricing or volume tiers published for any product. Informative Research: Platform naming on the site is imprecise, making scoping harder than it should. Point Predictive: No origination system or point of sale integration is named anywhere. Point Predictive: The half-of-applications and sixty percent claims carry no third-party validation. Point Predictive: Consortium depth is strongest in auto, with no equivalent mortgage scale disclosed.

Which one fits which shop

Best fit for Informative Research: Lenders that want credit and verification on one order with GSE rep and warrant relief. Best fit for Point Predictive: Lenders with enough application volume for score-based triage to change staffing.

What each entry concludes

Informative Research: Informative Research is Stewart’s credit and verification arm. Informative Research: Few suppliers can put credit, assets, income, employment and property data on a single order. Point Predictive: Point Predictive builds fraud risk models from a consortium data repository, and its centre of gravity is auto. Point Predictive: MortgagePass is the mortgage product, a score that rank-orders applications by the odds of material fraud.

The short answer

Informative Research finishes ahead on the published rubric, 4.3 to 2.1. The margin comes mostly from Integrations and ecosystem. Same rubric, same weights, no sponsorships.