Bring borrower data, financial evidence, policy checks, and reviewer context into one controlled credit decisioning workflow.
Mimasa AI supports underwriting automation without taking authority away from credit teams. It prepares source-linked analysis, surfaces exceptions, and routes each case for human review.
Support business, SME, commercial, consumer, secured, and mortgage lending with workflows built around your policies.
Borrower Evidence
Financial statements
Source retained · Ready for review
Bank activity
Source retained · Ready for review
Collateral record
Source retained · Ready for review
Policy version
Source retained · Ready for review
Underwriting Evidence Workspace
Human-ControlledLiquidity
Source linked
Policy tests
2 for review
Memo
Draft
Authority
Assigned
Decision Gate
Evidence → analyst review → authorised decision
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Every material observation should lead back to its source, rule, or approved model output.
Credit decisioning is the process used to assess a credit request and reach an authorised lending decision.
It combines borrower evidence, risk policies, analysis, approved models, exceptions, and professional judgment.
Automated credit decisioning can organise data, apply configured rules, and prepare a case for review. Mimasa connects the evidence, analysis, tasks, and approvals without replacing lender authority.
Credit teams often have the data they need. The evidence sits in different files and systems.
Analysts re-enter figures, spread statements, compare periods, check policies, and draft the same memo sections. Underwriting automation reduces this coordination burden.
Connect approved application, financial, transaction, bureau, collateral, exposure, and document sources. Group them under the correct credit case.
Classify each source by date, owner, type, and status. Flag missing or stale evidence before detailed analysis begins.
Extract configured line items from statements and schedules. Map them to the lender's approved financial model or spreading template.
Calculate only configured ratios and transformations. Keep the original value, mapped value, period, unit, and source visible.
Run approved rules against the prepared case. Show the rule, input, result, severity, and required reviewer.
Do not treat every exception as a rejection. Route it to the professional or committee authorised to assess it.
Generate a draft credit memo with evidence links, assumptions, exceptions, and open questions. Route it through the required review and approval path.
The final decision, override, conditions, and rationale belong to authorised employees.
Automated financial spreading can reduce repetitive entry across balance sheets, income statements, cash-flow statements, and supporting schedules.
Mimasa can map configured values into an approved structure. Reviewers can compare each mapped value with its source document.
AI financial statement analysis can explain period changes and highlight configured measures. Unclear values enter a review queue.
Automated bank statement analysis can organise transactions, totals, balances, recurring flows, and configured exceptions.
Automated cash flow analysis can compare patterns with submitted financial information. It may surface items that need explanation.
These prompts are not a credit verdict. They organise evidence for credit risk analysis.
Credit policy automation should make policy tests easier to trace. It should not turn policy into an unexplained black box.
| Policy Area | Evidence Used | Configured Test | Result | Required Action |
|---|---|---|---|---|
| Borrower eligibility | Approved customer and facility data | Lender-defined rule | Pass, review, or exception | Continue or assign reviewer |
| Financial threshold | Source-linked financial measures | Approved policy threshold | Within or outside range | Record explanation |
| Exposure | Connected exposure records | Lender-defined limit | Within or outside limit | Route to authorised owner |
| Collateral | Valuation and security records | Approved coverage rule | Complete, review, or missing | Request evidence or review |
| Documentation | Required case evidence | Checklist or rule | Complete or incomplete | Create a task |
Each rule result should display its version and source inputs. Overrides must record the authorised person, rationale, and time.
Credit memo automation can create a structured first draft from approved case information. It can refresh affected sections when source data changes.
An automated credit memo remains a draft until an authorised employee reviews it. Every material figure and statement should retain a source link.
Receive the prepared case from the loan-processing workflow or approved connected systems.
Extract, map, reconcile, and review configured financial information.
Bring together approved bureau, scorecard, exposure, transaction, collateral, and relationship information.
Apply lender-defined rules. Create named exceptions for results that need review.
Prepare a source-linked summary with calculations, risks, assumptions, and open questions.
An analyst checks evidence, corrects issues, adds judgment, and records recommendations.
The authorised person or committee approves, declines, returns, or applies conditions under the lender's process.
Record the decision, rationale, conditions, approvals, and permitted downstream hand-off.
Agentic lending can coordinate multi-step work across approved data, documents, systems, rules, and teams.
Agentic underwriting means coordinated work with controls. It does not mean an agent has independent lending authority.
A credit risk assessment considers evidence about a borrower, facility, repayment capacity, exposure, security, and policy fit.
AI credit risk assessment can organise evidence and identify review points. The lender defines approved data, models, rules, and decisions.
This traceability makes AI credit analysis easier to review and govern.
AI credit scoring and automated credit scoring are distinct from workflow automation. A score may come from a bureau, internal scorecard, or another approved model.
Mimasa can call an approved scoring service and record its response beside other case evidence.
Credit scoring automation does not make Mimasa the authoritative scoring model. The lender remains responsible for governance, validation, monitoring, and use.
| Lending Segment | Typical Evidence And Workflow Emphasis |
|---|---|
| Business And SME Lending | Entity information, bank statements, tax records, financial statements, owner context, cash flow, and policy exceptions |
| Commercial Lending | Detailed financial spreading, group exposure, collateral, covenants, facility structure, multi-level review, and committee decisions |
| Consumer Lending | Application data, approved bureau or scorecard results, income evidence, affordability inputs, policy checks, and exception review |
| Secured Lending | Borrower analysis, asset or property information, valuation records, coverage rules, conditions, and legal-document status |
| Mortgage Lending | Income, obligations, property, valuation, approved score outputs, policy rules, conditions, and human approval |
Business credit analysis may require more narrative and financial context than a standardised consumer case. Configure each workflow around the lender's approved process.
Credit analysis software may provide spreading, ratios, scorecards, or risk models. Loan origination platforms may own applications, product rules, and decision records.
Mimasa does not need to replace these systems. It connects approved data with documents, analysis, policies, tasks, agents, and human approvals.
Mimasa is not a credit bureau, scoring model, loan origination system, core banking platform, risk engine, or policy owner.
Credit policies, risk appetite, overrides, pricing, approval, rejection, and final decisions remain with authorised professionals.
Filter configured measures by product, segment, team, and review stage.
Metric definitions depend on the lender's data and operating policy. Mimasa does not present invented outcomes.
Choose one lending segment with stable policies, known evidence, and enough case volume to test the workflow.
Credit cases contain sensitive customer, business, financial, and risk information. Access and action must remain controlled.
Mimasa supports cloud, private-cloud, and on-premise deployment. Banks decide which users and agents can access each source, tool, case, and action.
Discuss Security And Deployment RequirementsCommon questions about credit decisioning, financial spreading, risk analysis, scoring boundaries, credit memos, and human control.
Extract configured values and tables from financial and supporting documents.
Standardise approved data for analysis and reporting.
Control access, datasets, policies, permissions, and traceability.
Monitor cases, exceptions, workload, and trends.
Build agents that use approved sources and tools within defined limits.
Coordinate checks, tasks, exceptions, reviews, and approvals.
Keep analyst, reviewer, committee, task, and case context together.
Explore governed intelligence and automation around existing banking systems.
See the connected intelligence layer across BFSI.
Bring governed analytics into credit underwriting decisions.
Connect financial-statement extraction and source-linked memo preparation.
Prepare reviewed financial documents and source-linked values for underwriting analysis.
Continue from application preparation into evidence-led underwriting.
Browse enterprise AI workflows by industry and function.
Connect evidence, approved analysis, policy tests, exceptions, and human authority in one governed workflow.
