Truv vs Point Predictive

What the rubric says

Truv and Point Predictive are both scored in Verification & Data. Truv carries an overall of 4.7, 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. Truv takes it, 4.9 to 1.6.

Where the five axes separate

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

Pricing posture

Truv does not publish pricing. Its listed model is quote only, volume-tiered, with published savings guarantees but no rates. Point Predictive does not publish pricing. Its listed model is quote only.

Deployment and who each one targets

Deployment for Truv: Cloud, API with LOS and POS integrations. Deployment for Point Predictive: Cloud, delivery mechanism not published. Segment focus for Truv: Independent mortgage banks and credit unions attacking verification cost by displacing or front-running The Work Number. 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

Truv: Authorized for Fannie Mae DU validation and Freddie Mac LPA AIM, relief included. Truv: Unusually long named integration list, from Encompass and Dark Matter Empower to Tidalwave. Truv: Payroll data, bank data, document fallback and pre-closing reverification under one contract. 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

Truv: No rates published; savings guarantees measure against a baseline Truv never defines. Truv: Coverage and conversion figures, 96 percent workforce coverage included, are company claims. Truv: The 35 and 70 percent guarantees need annual commitment or monthly minimums. 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 Truv: Lenders whose verification spend has become a line item worth fighting. Best fit for Point Predictive: Lenders with enough application volume for score-based triage to change staffing.

What each entry concludes

Truv: Truv exists because The Work Number got expensive, and the marketing says so without embarrassment. Truv: The site promises up to 80 percent savings against TWN and a Professional tier with a 35 percent. 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

Truv finishes ahead on the published rubric, 4.7 to 2.1. The margin comes mostly from Integrations and ecosystem. Same rubric, same weights, no sponsorships.