Lending & Credit
We build the software behind credit decisions. Origination, automated underwriting, decisioning, and servicing run as one pipeline, so a lender can decide in minutes and show the reason behind every answer. Document-heavy manual review becomes an auditable record that examiners, borrowers, and your own risk team can follow.
How we help
The outcomes a lender feels once credit runs as one auditable pipeline.
Decide in minutes
Manual credit review moves an application through queues, email threads, and re-keyed data before anyone reaches a verdict. We wire the pull, parse, and score steps into one pass, so a clean file clears in minutes and staff spend their hours on the borderline cases that need a human read.
Defend every decision
When a regulator or a declined applicant asks why, most lenders reconstruct the answer from scattered logs and memory. Each decision we build carries its inputs, the model version, and the reason codes that produced it. Adverse-action notices under ECOA and Reg B draw from that same record, so the explanation matches what the system did.
Say yes to more good borrowers
Thin-file and self-employed applicants often fail a scorecard that only reads traditional bureau data, so a lender turns away accounts that would have paid. We add cash-flow, rent, and payment histories as scored inputs under documented governance, which widens approval without dropping the audit trail that fair-lending review depends on.
What we deliver
One credit system, built in parts your team and your examiners can read.
Loan origination (LOS)
Paper applications, re-keyed borrower data, and stalled hand-offs between sales and credit stretch a simple loan into weeks. We build the origination system that captures the application once, verifies identity and income against source data, and routes each file by product and risk. Every status change lands in one record your team and auditors can read.
Automated underwriting & decisioning
Underwriters who copy figures between a bureau report, a spreadsheet, and a policy PDF make slow decisions that are hard to reproduce. We encode your credit policy as versioned rules and models, so each application runs the same checks in the same order. Approve, decline, and refer outcomes come back with the reason codes attached.
Credit scoring & risk models
Off-the-shelf scorecards rarely fit a lender's own book, and a black-box model leaves risk teams unable to explain a number to examiners. We build and validate scoring models on your data, document each feature and its weight, and track drift after launch, so the score stays explainable and fair-lending review has something to inspect.
Servicing & collections
After funding, payment tracking, hardship handling, and delinquency work often sit in separate tools that never agree. We build servicing that keeps the loan ledger, payment schedules, and borrower contact in one place, with collections workflows that follow your policy and FDCPA limits. Each borrower touch is logged for dispute and audit review.
BNPL & point-of-sale finance
Point-of-sale credit has to approve or decline inside a checkout that a shopper will abandon in seconds. We build the decision service that scores a split-pay or installment request in that window, checks affordability, and returns terms the merchant can show at once. Settlement, refunds, and repayment tracking run behind it on the same ledger.
Alternative-data credit
A bureau file alone leaves thin-file, young, and immigrant applicants looking riskier than they are. We bring cash-flow, rent, telecom, and payroll data into the decision under consent and documented governance, score it alongside traditional inputs, and keep the reason codes clean, so wider approval does not cost you adverse-action or fair-lending defensibility.
Our technology radar
Where we stand today. The Hold ring is the one that matters: these are positions we argue against, including when a client asks for them.
The default unless there is a reason not to.
- Explainable models for any decision subject to adverse-action review
- A human in the loop at the decision point
- Confidence-routed document extraction
- Policy as versioned data
- A decision log holding inputs and model version
We use these on new work and watch them closely.
- Cash-flow scoring for thin-file borrowers
- LLM-assisted extraction of rules from underwriting manuals, confirmed by a person
- On-prem models where documents cannot leave the network
We argue against these.
- Black-box scoring in regulated credit
- Per-template document parsers, which break on the first new layout
- Auto-approval with no review path
- Training on customer data without a governance record
Every decision carries its own paper trail
Inputs, model version, and reason codes travel with each answer, so a review months later reads the same as the moment of decision.
How we work
Discover
Systems, constraints, and the regulation you operate under get mapped before implementation starts, so the design accounts for what already runs and for what examiners will ask about.
Architect
A design that fits your stack. Integration-first, self-hostable, and built to change as rules, regulation, and volume do.
Build
Senior engineers ship in tight increments, each tested and reviewed as it goes, so the system is reviewable at every step instead of only at the end.
Harden
Security, compliance, and load-testing run inside the build, so controls, audit trails, and peak-volume behavior are proven before launch.
Run
The handover includes clean, documented systems, with the option to keep the same engineers operating them once they are live.
Why Oxagile
Two decades building regulated software
We have shipped software for finance and other regulated fields for over twenty years. That history shows in how we treat credit systems, where a wrong number carries legal weight. Our teams have sat through audits and know what evidence an examiner asks for, so controls get built in rather than bolted on later.
Explainability built into the model
We treat the reason for a decision as a required output that ships with the score. Every rule we build records its inputs and produces reason codes a person can read. When a borrower or regulator asks why, your team has the answer already, and fair-lending review has a documented feature set to inspect.
Compliance as part of the build
Credit software lives under ECOA, Reg B, FCRA, and state licensing rules. We map those obligations during design and write the controls, audit trails, and adverse-action logic into the system as we build. Compliance review then confirms working behavior instead of chasing gaps after launch, which shortens the path to a defensible go-live.
You own what we build
The systems we deliver are documented and self-hostable, so you keep the code, the models, and the data. There is no lock to an opaque system you cannot audit. When the build ends, you can run it with your own staff or keep the same engineers who wrote it operating the system.
What our architects hand you
Delivered from Presales and Discovery, yours to keep whatever you decide next.
Book an architecture sessionSystem Design
Target architecture against your systems, with failure domains and the order things get built.
API contracts
Every integration point written before an implementation exists.
Compliance scope assessment
Which systems fall in scope under each option, and what the design removes.
Cloud cost model
What it costs to run at your volume, by component.
Security review
Threat model for the paths that move money or hold personal data.
Migration and cutover plan
Parallel run, reconciliation, gradual shift, and a rollback point at each step.
Questions
What lenders ask us before a credit build starts.
Do you build lending systems or do you operate them too?
We do both. Most engagements start as a build, where senior engineers design and ship the origination, decisioning, or servicing system on your stack. After launch you choose the model. Your own team can run it from our documentation and handover, or the same engineers who built the system stay on to operate and change it as volume and rules move.
How do you keep automated decisions explainable for regulators?
Each decision records the data it read, the model or rule version that ran, and the reason codes it produced. That record backs the adverse-action notice a declined applicant receives under ECOA and Reg B. Risk and compliance teams can replay any past decision and see the same result, which gives examiners a documented trail rather than a reconstruction after the fact.
Can alternative data widen approvals without breaking fair-lending rules?
It can, with governance in place. We bring cash-flow, rent, and payroll data in under borrower consent, document each feature, and test the model for disparate impact across protected classes before it goes live. The reason codes stay readable, so an applicant still gets a clear adverse-action explanation. We do not promise a fixed approval lift, since that depends on your book.
Will this connect to our core, bureaus, and existing tools?
Yes. We design integration-first and have wired credit systems to core banking, the major bureaus, KYC and fraud providers, and payment rails. Where a documented API exists we use it, and where one does not we build the adapter. The origination or servicing system reads and writes your systems of record instead of becoming another silo your staff reconcile by hand.
How long before we see a working system in production?
Senior engineers ship in tight increments, so you review working software within the first weeks rather than at the end. A focused decisioning or origination build reaches production in a few months, and larger programs land in stages, each one tested, reviewed, and defensible on its own. The exact timeline depends on your integrations, data, and regulatory scope.
Lending, broken down
Each of these goes deeper than this page does: the systems we build, how they are put together, and what we hand over.
Underwrite faster,
without the manual read.
Bring us the bottleneck, whether it is decisioning, document processing or servicing, and we'll scope the system that clears it.