Built for Regulatory Evaluation Deterministic White‑Box

Deterministic credit intelligence for regulated lenders and trade credit providers.

Loan Facilitation Engine (LFE) is a deterministic credit intelligence platform that helps banks, NBFCs, and trade‑credit providers apply transparent, configurable underwriting policies with full auditability and explainability.

Designed for parallel-run validation before production deployment.

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4‑stage

Underwriting integrity

SHAP

SHAP‑Based Explainability

Real‑time

Real‑Time Scoring

RCI

Bank full control

LFE ADVISORY REPORT (SAMPLE)explainable

Risk Grade:B+ (PD Band 2.4%)

Cash Flow Strength:82/100

Concentration impact:-12 pts (single source 68%)

SHAP loaders: stability −0.08 | behavioral −0.03 | liquidity +0.11

Audit trail • Non‑binding recommendation

WHY BANKS CHOOSE FISENSE

Tangible benefits for your credit portfolio

Deterministic white‑box engine delivers measurable improvements: speed, consistency, and portfolio performance.

Faster Underwriting Workflows

Automated cleansing & real‑time scoring reduce decision time from days to milliseconds.

Consistent Policy Application

Reduce decision inconsistency through deterministic policy execution.

Portfolio Profit Uplift

Supports improved portfolio quality through stress testing, concentration limits, and early warning signals.

Standardized Decisioning

Single source of truth for credit committees — fully reproducible audit trail.

Stress‑tested underwriting

50% income shock simulation, DSCR floors, automatic limit reduction if breached.

Fully configurable risk appetite

Adjust concentration thresholds, pillar weights, and credit multipliers — no black box.

Deterministic 4‑stage underwriting

Cleansing → Multi‑dimensional scoring → Mandatory overrides → Synthesized SHAP profile

The engine applies deterministic rules and policy weights. SHAP is used solely to report feature contribution – it does not replace or override the deterministic policy framework.

Cleansing Engine

Internal transfer removal, income pattern classification (Stable/Cyclical/Lumpy/Concentrated).

Multi‑Dim Matrix

5 risk pillars: Cash Flow (25%), Stability (20%), Behavioral (25%), Liquidity (15%), Credit Seeking (15%).

Override Layer

Circuit breakers (concentration >75% → max grade C) + stress failure test.

SHAP Explainability

Audit trail, risk loaders, PD band, limit calibration — fully explainable.

Designed for bank‑grade trust

Every layer of the LFE is built with transparency, auditability, and regulatory alignment — no black boxes, no vendor lock‑in on decisions.

Every recommendation generated by the platform can be reconstructed, reviewed, and independently validated.

Data isolation & tokenization

Bank CIF → HMAC‑based user_token (irreversible). Sensitive customer data retained only for operational requirements and institutional policy.

Immutable audit logs

Retention periods configurable to institutional policy and regulatory requirements. Read‑only regulator interface (RCI).

Model drift & fallback

Population Stability Index (PSI) monitoring. Automatic fallback to rule‑based scoring if drift exceeds threshold.

Regulatory Control Interface

Kill switches, policy overrides, segment caps, default rate thresholds — all bank‑controlled.

Advisory‑only architecture

No final credit decisions, no funds custody, no deposit taking. Balance sheet stays with the bank.

Independent validation ready

Model backtesting, SHAP explainability, and full auditability for internal risk teams and regulators.

FiSense is pursuing participation in the State Bank of Pakistan Regulatory Sandbox. Our technology processor model aligns with SBP’s expectations for vendor‑processor relationships.

TARGET AUDIENCE

Who is FiSense for?

Our deterministic engine empowers two core constituencies: regulated lenders and non-financial corporates extending trade credit.

Banks & NBFCs

Why? Traditional credit scoring models are opaque, inconsistent, and fail under regulator scrutiny. FiSense gives lenders a deterministic, fully explainable underwriting layer that supports improved portfolio quality, reduces decision inconsistency, and provides audit-ready decisions. With our parallel-run model, banks can test LFE alongside existing processes without risk. The result: faster decisions, consistent policy execution, and full auditability.

Improved Risk Visibility Regulatory ready Portfolio Risk Insights

Manufacturers & Importers (Trade Credit)

Why? Offering trade credit can improve customer conversion and purchasing capacity, but manual credit checks are slow and risky. FiSense provides a lightweight, rules-based scoring system that helps manufacturers assess the creditworthiness of their buyers using anonymized financial behaviour, bank statement analytics, and trade references. Embed our engine to offer supplier credit confidently, improve receivables visibility, and support controlled revenue growth.

Unlock B2B revenue Improve receivables visibility Non-recourse advisory

Industry‑specific deployments

All deployments use the same deterministic core engine, with configurable sector‑specific policy parameters.

Motor Vehicles

Dealer financing, floor plan validation, retail auto loans – real-time risk scoring for showroom credit.

Health / Pharma

Hospitals & distributors: evaluate clinic credit lines, equipment financing, drug supply chain risk.

FMCG

Distributor credit scoring, retailer working capital limits, promotion-linked advances.

Textiles & Apparel

Export order financing, supplier credit for fabric & raw materials – seasonality adjusted scoring.

Construction

Contractor financing, material supplier credit, project cashflow-based underwriting.

Electronics Retail

POS financing for mobile/electronics – instant decisioning at checkout.

Agri-Inputs

Seed/fertilizer credit for farmers – behavioral scoring via digital records.

Our products

Modular, deployable credit intelligence suites for financial institutions and corporates.

FiGlobal

Deterministic white‑label LFE

Loan Facilitation Engine + lightweight LOC (Line of Credit) – fully branded for your institution. Includes RCI, SHAP explainability, 4-stage underwriting, and optional integrated line‑of‑credit recommendation module. Banks retain full discretion; engine provides advisory limits, stress tests, and concentration alerts. Deploy as API-first or on‑prem instance.

White‑labelLOC readyAudit trail

FiReview

Anonymised historical comparison

Benchmark internal portfolio segments and monitor underwriting performance trends. Industry benchmarking capabilities available where sufficient anonymized comparison datasets exist. FiReview uses privacy-preserving tokens to compare key risk metrics: default rates by segment, cashflow volatility, concentration ratios.

Privacy-firstBenchmarkingSaaS

FiSale

Point‑of‑Sale credit offer

Two modes:
1) Bank‑powered: up to 60% financing facilitated by partner bank (advisory-only scoring).
2) In‑house: configurable supplier credit limits determined by institutional policy, using FiSense deterministic scoring to approve trade credit instantly. Real‑time decisioning at checkout – reduces abandonment, lifts average order value.

POS embeddedConfigurable limitsInstant approval

Why now?

Market forces and regulatory shifts make deterministic credit intelligence essential.

Regulatory Expectations

Greater focus on explainability, governance, and auditability. Black‑box models face increasing scrutiny. FiSense delivers full transparency by design.

Credit Expansion

Growing demand for SME, consumer, and trade‑credit products. Institutions need scalable, consistent underwriting to capture opportunity without undue risk.

Operational Efficiency

Institutions seek consistent underwriting without increasing manual review effort. Automate policy execution while keeping human oversight where it matters.

Financial institutions are under increasing pressure to improve underwriting consistency, governance, and operational efficiency. FiSense is designed to support that transition.

About FiSense.AI

Banking practitioners, risk specialists, and technologists — united to rebuild credit underwriting for the digital age.

Salman Zafar

CEO & Co-Founder

Salman brings over a decade of experience at the intersection of banking and technology. Having held senior roles in credit risk and digital transformation, he saw firsthand how black‑box scoring models create friction, opacity, and regulatory risk. He founded FiSense to give banks back control — with a deterministic, explainable engine built for sandbox validation.

Deep Banking DNA

Team members with experience across banking, risk, compliance, and fintech.

Experienced fintech engineers and data scientists

Building deterministic systems for financial institutions

Our Partnership Model: Parallel Run

We don't ask you to rip out your current system. We propose a parallel run. FiSense.AI will sit alongside your existing underwriting process — scoring the same applicants, using the same data. You’ll see the results side‑by‑side, with no disruption to live operations.

No production decisions are altered during the evaluation phase.

What you get:

Compare default predictions vs actual outcomes
Full transparency — every score explained via SHAP
Low‑friction pilot deployment

The team is well-versed with SBP, SECP requirements, and understands the regulatory boundaries for vendor‑processor models.