Financial Services
Build an AI-powered credit risk and loan default analysis tool
Kliv is an AI app builder. Describe your credit risk and loan default analysis process, and it builds a real app for loan records, watchlist rules, dated grades, review queues, dashboards, and policy-grounded answers.
Just enter your idea into the text box and AI will build it for you
What is Kliv?
Kliv isn’t a black-box scoring product — it’s an AI that builds software for you.
You describe the app your lending team needs in plain words, and Kliv builds it: the database, loan records, watchlist rules, review queues, memos, dashboards, access rules, and policy-grounded agent. You own it, run it on your own accounts, and change it later by asking. It isn’t a template.
On this page, that app is a credit risk and loan default analysis tool. Kliv builds all kinds of web apps; this page focuses on structuring the risk process and keeping the decision trail.
Why the loan book needs rules around it
A lender may already have early-warning signals: late payments, stale borrower financials, breached covenants, and changed grades. The problem is often that those signals live across several systems and a shared drive.
Kliv builds the structure around the loan book. Loans, borrower files, grades, watchlist rules, review assignments, memos, and dashboards become records your team can query and audit.
The columns behind the grade
The records that make a risk process defensible.
The loan
Terms, collateral, rate, and every payment are stored as dated rows, so “twelve days late” is computed from records.
The borrower file
Financial statements upload on their covenant schedule. A missing statement is itself a signal, and the app knows how stale it is.
The grade
A grade from a score, bureau feed, or analyst judgment lands as a dated row with an author. History is kept rather than overwritten.
The watchlist
Rules you state in plain terms, such as two late payments plus stale financials, can run nightly and promote loans for review.
The review queue
Every flagged loan becomes an assignment with the file attached. Memos, decisions, and dissents stay on the loan with names and dates.
The policy agent
The agent answers analysts from your credit policy and past committee memos, citing the section, while seeing only loans that analyst may see.
An example: Omar’s equipment-finance lender
Here’s how one lending team might use it. It’s only an example — you would describe your own portfolio, signals, policies, review roles, and reporting needs.
A signal promotes a loan
A $148,500 equipment loan pays 12 days late in the same month a covenant test fails. The nightly rule run promotes it to the watchlist.
The review queue brings context
The loan appears on a senior analyst’s list with borrower files, payment rows, covenant history, and grade history attached.
The memo cites policy
The analyst drafts the review memo. The agent supplies the credit-policy section on covenant remediation and relevant committee precedents, with citations.
The decision keeps history
Committee downgrades the loan and sets a remediation plan. The new grade is a fresh row beside the old one, with author, date, and rationale.
The examiner can follow the trail
A year later, the team can answer who changed the grade, when, and on what basis by querying the loan record.
Describe the risk tool you want
Kliv builds from your description, so include loan records, data sources, watchlist rules, assignments, memo requirements, access roles, and dashboards. Here are three examples:
Loan watchlist dashboard
Signals promoted nightly by rule.
“Build a credit risk and loan default analysis tool for our equipment-finance team. Track 600 loans with terms, collateral, payment history, borrower financials, covenant schedules, dated risk grades, and watchlist rules for late payments and stale financials. Flagged loans should land in review queues with assignments.”
Covenant tracking portal
Financials due, chased, and dated.
“Build a covenant tracker for our commercial lending team. Each loan should list reporting requirements, due dates, borrower uploads, analyst assignment, overdue-statement alerts, and a dashboard showing stale financials by officer and portfolio segment.”
Committee memo hub
Grades and rationales on the record.
“Build a credit committee tool. Analysts should draft review memos on flagged loans, attach policy citations, record decisions as new dated grades with author and rationale, and keep the full grade history on each loan.”
Built like the audit expects
Grades are append-only history. A grade change adds a row instead of editing the past. That keeps the process trail clear.
Sight lines follow roles and assignments. A junior analyst can read her assigned book; the chief credit officer can read the portfolio; borrowers are not users in this tool. The database applies those rules to reports and agent answers.
The policy agent stays inside the binder. It answers from the credit policy and memos you load, cites section and page, and stores each exchange on the loan it concerned.
Concentration can be a live page. Exposure by sector, collateral type, and grade band can come from the same rows the queue reads, current every morning.
A credit desk rarely stops at one tool; internal tools on Kliv is where the rest of the middle office’s queue gets built.
Common questions
What exactly is Kliv?
Kliv is an AI app builder. You describe the credit risk app you need, and Kliv builds the loan records, rules, queues, dashboards, access, and policy-grounded agent.
Do I get a real app, or a template?
A real app. It is built around your portfolio, risk rules, review roles, committee process, and reports.
Can I change it after it’s built?
Yes. You can add fields, change watchlist rules, add reports, revise roles, or update memo workflows by asking.
Does Kliv predict which loans will default?
No. Your scores come from your own scoring process, bureau feeds, or analysts. Kliv stores them with dates and authors, applies your rules, and keeps the decision trail.
Where does the portfolio data live?
In the app’s own database, where your team can query, export, and reconcile it against the core system when auditors ask.
Can we control who sees borrower financials?
Yes. Assignment and role decide sight lines, and the rules hold across pages, searches, exports, and agent answers.
Can the agent write a committee memo?
It can draft from the loan’s rows and your policy documents, citing both. The recommendation and signature stay human.
Can watchlist rules run automatically?
Yes. Rules can run on a nightly schedule and promote loans into review queues when signals match.
Can dashboards show concentration risk?
Yes. Exposure by sector, collateral type, and grade band can be computed from the same loan rows.
Can I move off Kliv later?
Yes. The app’s code syncs to your own Git repository, and your data can be exported.
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Put the signals in one place
Describe the portfolio, borrower files, watchlist rules, review queues, memo trail, access roles, and dashboards. Kliv builds the risk tool around them.
