Deterministic · Auditable · Purpose built for regulated lenders

The operating system purpose built for every function in a lending business.

Parse replaces the fragmented intelligence stack — ETL, warehouse, BI, data science, ML ops, reporting — that runs Credit, Risk, Collections, and Finance at every regulated lender. One platform. Auditable by design. Live in under a week.

Book a working session → See the platform
/ Why lenders move to Parse

Four shifts that change the economics of a lending business.

< 1 wk
End to end implementation.
Industry standard is 10 to 12 months.
< 30 min
Build, validate, deploy any predictive model.
Versus three to four months.
Deterministic
& Auditable
Unlike LLMs, Same data, same answer, every run.
Full data lineage on every output.
Pre Embedded
Intelligence
Functional and regulatory intelligence built in for every lending function.
Out of the box, not from scratch.
01
/ The Problem

Four functions. Five tech layers. One bottleneck.

Credit, Risk, Collections, and Finance each make decisions every day to optimize your loan portfolio performance. To make those decisions, they all sit on top of the same fragmented intelligence stack — five distinct technology layers, each with its own vendors and specialists.

/ Who uses what

The same fragmented stack runs every function.

Credit underwrites. Risk monitors. Collections recovers. Finance reports. Each one draws intelligence and executes workflow across multiple layers of disconnected vendor systems. Redundant data, parallel manual work, four teams doing the same thing differently. CapEx and OpEx heavy, over a year to implement, months to update anything.

/ What it costs

Decisions get slower as the stack gets bigger.

One predictive model takes three to four months to build. Every product change and every regulatory update takes months to implement in these systems.

Despite this exhaustive intelligence infrastructure, NPLs and cost to income stays high. None of the incumbents offer the speed or agility this era demands.

The lending intelligence stack today / DIAGRAM
/ Consumers · 4 functions
Credit
Scoring · Underwriting · Pricing · Propensity
Risk
PD / LGD / EAD · EWS · Basel
Collections
Delinquency prediction · Treatment · Recovery
Finance
ECL · IFRS 9 · Loss forecasting · Capital · NIM
/ Fragmented intelligence stack · 5 layers
Layer 5
Reporting Layer
MIS reporting · Regulatory reporting tools · Dashboards & BI
Layer 4
Data Science Layer
ML models · Feature stores · ML ops & model hosting
Layer 3
Analytics & Workflow
Risk engine · ECL / IFRS 9 engine · Credit underwriting · Collections
Layer 2
Data Warehouse
Enterprise Data Warehouse · Data Marts
Layer 1
ETL & Data Integration
ETL pipelines · Data integration · Connectors
5 layers · many vendors · separate teams · separate data lineages
/ Foundation · System of Record
LMS · LOS · Core Banking
Your system of record · loans, accounts, transactions, repayments
Every decision your loan book makes is filtered through this five layer stack.
02
/ The Parse.ai Platform

One integrated platform replaces all five layers.

Parse.ai sits on the same foundation — your LMS, LOS, and core banking system — and collapses the entire five layer intelligence stack into a single deterministic platform with three core products. No warehouse to build. No pipelines to maintain. No specialist team for each layer.

From five fragmented layers to one integrated platform / DIAGRAM

Before · 5 layers

Reporting Layer
Data Science Layer
Analytics & Workflow
Data Warehouse
ETL & Data Integration

After · 1 platform

Parse·ai
Deterministic intelligence platform
01
Predictive Analytics
02
Risk & Regulatory Intelligence
03
Credit Underwriting
Same foundation for both
LMS · LOS · Core Banking System
Parse does not replace your system of record · it sits on top of them
One platform. One data lineage. One source of truth for every decision.
Product 01

Parse Predictive Analytics

ML models in minutes, not months.

Drag in your loan tape. Define the outcome. Parse handles feature engineering, model selection, training, validation, and deployment. The team that owns the strategy owns the model.

/ Use cases
  • Default & bounce prediction
  • Roll forward & cure rate
  • Collections response
  • Cross sell & top up propensity
  • Foreclosure early warning
Built to comply with SR 11-7 principles
Full data lineage on every model. Independent validation framework. Auto generated model documentation. Continuous performance monitoring. Model inventory by design.
Product 02

Parse Risk & Regulatory Intelligence

The reporting and intelligence layer for Risk, Finance, and Collections.

Pre embedded lending metric catalog covering portfolio analytics, risk monitoring, and regulatory reporting. Risk runs PD, LGD, EAD, and EWS. Finance runs ECL, capital, and NIM. Collections runs PAR, roll rates, and vintage curves. All from the same data, in natural language.

/ What's covered
  • IFRS 9 & Ind AS 109 staging and ECL
  • PD, LGD, EAD analytics
  • Early warning signal monitoring
  • Roll rates, PAR, vintage curves
  • Regulatory submissions ready
Product 03

Parse Credit Underwriting

SME credit decisions in minutes.

Ingest financial statements, bank statements, and tax returns. Parse produces a complete Credit Assessment Memo with anomaly and fraud detection, projection validation, scenario analysis, and industry benchmarks.

/ What you get
  • Full Credit Assessment Memo from raw documents
  • Anomaly & fraud detection
  • Projection validation and reasonableness checks
  • Industry benchmarking against your own portfolio
  • Covenant monitoring through the loan lifecycle
03
/ Value Proposition by Function

Built for the people running your loan book.

Four functions. One unifying capability that runs through every one of them.

/ Who this is for
Heads of Credit, Risk, Collections, Finance, and Technology at banks, credit unions, NBFCs, and fintech lenders.
The unifying
capability
Every business team builds the model. Build, test, and deploy any predictive model in under an hour — no data science queue, no engineering ticket, no quarterly waiting. The strategy and the model live with the team that runs them.
01 · Sales & Origination

Target better. Grow faster.

— Acquire & expand
What this unlocks for you
Your sales and marketing teams build their own propensity models in thirty minutes. Cross sell, top up, channel response, behavioural scoring — owned by the team that runs the campaign, not by a six month data science backlog. Test a campaign hypothesis Monday, deploy it Tuesday.
/ What's in it
Cross sell propensity. Top up and renewal scoring. Behavioural credit scores for pre approved offers. Disbursement analytics. LTV and IRR cuts on originated pools. On demand dashboards through natural language.
/ Outcome
2 to 3x lift in cross sell and top up conversion. Lower acquisition cost through high propensity targeting. Right product, right customer, right moment.
02 · Credit & Underwriting

Originate better. Lend smarter.

— Decide with confidence
What this unlocks for you
Change credit policy in minutes, not quarters. Tighten a DSCR cutoff. Add a new behavioural rule. Build a segment specific PD model. Stress test the change against your live portfolio and see the impact on volume, approval rate, and expected default before you ship it. Iterate weekly. Stop shipping policy you cannot measure.
/ What's in it
Full Credit Assessment Memo from raw documents. Anomaly and fraud detection. Industry benchmarking. Projection validation. Scenario stress testing. Application PD, behavioural score, foreclosure prediction.
/ Outcome
Models built and deployed in under one hour. Dynamic algorithms to detect credit risk and anomalies. Aligned with SR 11-7 and IFRS 9 principles.
03 · Collections

Collect smarter. Lose less.

— Recover earlier
What this unlocks for you
Rebuild your bounce and roll forward models the day macro shifts. Test which treatment (call, SMS, payment link, restructure) works for which borrower segment. Simulate "what if I move twenty agents from bucket A to B" before you do it. A/B test new collections strategies and see results in days, not quarters.
/ What's in it
Bounce prediction up to thirty days ahead. Roll forward scoring. Response propensity by channel. Foreclosure early warning. PAR, roll rates, stabilisation and normalisation, all auto generated and filterable.
/ Outcome
20 to 35 percent improvement in collections efficiency. Lower cost per recovery. Lower provisioning through earlier resolution.
04 · Risk & Finance

See the full picture. Act with confidence.

— Report & defend
What this unlocks for you
Run any ECL, capital, or NIM scenario in minutes — not the quarterly analyst sprint. Stress test before management asks. Simulate the impact of a new product launch on portfolio risk before it goes live. Build a fresh ECL scenario the day the regulator publishes new guidance. Generate regulatory submissions on demand.
/ What's in it
Vintage curves and cohort analysis. IFRS 9 staging and ECL. PD, LGD, and EAD analytics. NIM decomposition. Natural language access to any metric or segment. AI Data Transformation handles ETL and schema mapping without code.
/ Outcome
Reporting cycle from days to minutes. Regulatory reporting out of the box. Self serve any view without raising a ticket. Teams build capability, not vendor dependency.
04
/ The Moat

Built for regulators, not for demos.

Most AI products in lending hand the heavy thinking to an LLM. That breaks the two things every regulator cares about: consistency and auditability. Parse is built the other way.

Principle 01

Deterministic

Same data in, same answer out. Every time. Every user. Every run. No probabilistic drift. No invented numbers.

Principle 02

Auditable

Open the calculation at any step. Show the regulator the input, the process, and the output. Defensible by construction.

Principle 03

Pre embedded

Lending intelligence built in. Regulator aligned models, metrics, and rules ready out of the box. Not a generic AutoML wrapper.

05
/ The Team

Built by people who have run this stack from the inside.

Two founders. Forty plus years between them in lending technology, credit risk, and analytics across the US, India, and Southeast Asia.

NT

Nanda Thiruvengadam

Co founder · CEO & CPO

Twenty plus years building lending, risk, and analytics platforms at scale. Co founded and ran Lend East, where he underwrote 150 plus fintechs and tracked over a billion dollars in AUM across alternative lending portfolios in Southeast Asia. Before that, led KPMG's Risk Analytics Advisory practice with 80 plus data scientists and 30 million dollars in revenue.

/ Credentials
  • Co founder & CPO, Lend East
  • Underwrote 150+ fintechs · tracked $1B+ AUM in alternative lending portfolios
  • Led KPMG Risk Analytics Advisory · 80+ data scientists · $30M+ revenue
  • Launched KPMG Risk as a Service SaaS · $1M ARR
  • Built 50+ proprietary credit algorithms · provisional patent in Singapore
  • Advisor to Singapore and Bahrain regulators on credit analytics
R

Radhakrishnan (Randy)

Co founder · CTO

Twenty plus years in lending and wealth technology. Built Lend East's proprietary private debt analytics platform end to end. Onboarded and credit rated 180 plus ventures across Southeast Asia. Has built and scaled platforms across lending, wealth, and insurance.

/ Credentials
  • CTO, Lend East · built proprietary debt analytics platform from scratch
  • 180+ ventures onboarded and credit rated across Southeast Asia
  • Built and managed a robo advisory platform end to end
  • Designed digital transformation for a Dubai based insurance firm
  • 20+ years in lending and wealth technology
/ See it on your own data

See Parse on your own loan tape.

We bring the platform. You bring a slice of your data. In one working session you will see what your decision stack will look like in under a week.

Book a working session → Explore the platform
Or write to us directly · sales@theparse.ai