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    2022-07 — 2025-02fintech-banking

    Loan Origination Pipeline for a Bank

    FintechMicroservicesTemporalAI

    🏦 Project: Loan Origination Pipeline for a Bank

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    📌 Context (as-is state)

    We started by working on a legacy loan process system in a bank.

    The system was a classic monolith that combined:

    • loan application intake
    • client verification
    • collateral processing
    • credit dossier assembly
    • deal origination
    • integrations with external services (Credit Bureau, collateral registries, CRM, agent's workstation)

    System problems:

    • tight coupling of modules within the monolith
    • long application processing time
    • lack of transparent stage control
    • frequent failures during integrations
    • inability to scale individual stages (e.g., collateral verification)
    • difficulty implementing new rules and products

    In essence, it was a "large data pipeline" that failed with any instability in one of its components.

    🎯 Transformation Goal

    We set the following goals:

    • decompose the monolith into independent domain modules
    • provide a managed business process orchestrator
    • enhance the stability of the loan origination pipeline
    • implement AI-powered document processing automation
    • make the system scalable at each stage

    🧩 Domain Decomposition

    We divided the system into independent domain boundaries:

    1. 📥 Application Intake

    • a single entry point for applications (CRM / partners / agent's workstation)
    • data normalization
    • primary validation
    • application deduplication

    2. 🧾 Client and Collateral Check (Risk & Collateral Check)

    • integrations with external sources (Credit Bureau, registries, anti-fraud)
    • client scoring
    • collateral property valuation
    • calculation of limits and conditions

    3. 🤖 Dossier Assembly using AI (Document Intelligence)

    One of the key transformation modules.

    We implemented an AI pipeline:

    • document upload (PDFs, images, scans)

    • OCR and entity extraction

    • LLM processing for:

    • document classification

    • extraction of key fields (Full Name, amounts, dates, collateral objects)

    • completeness check of the dossier

    • automated generation of a structured credit dossier

    Result:

    • reduction in manual document review
    • decrease in errors during dossier assembly
    • acceleration of the deal preparation stage

    4. 📦 Case Assembly Module

    • collection of all verification results
    • aggregation of data from different domains
    • construction of a unified credit case
    • data completeness control before the final decision

    5. ✍️ Deal Origination

    • generation of contracts and loan documents
    • integration with EDMS (Electronic Document Management System)
    • final agreement on terms
    • recording the transaction in the bank's accounting systems

    ⚙️ New Solution Architecture

    🧠 Orchestration: Temporal

    We used Temporal as the core process management engine:

    • each loan = a separate workflow

    • each stage = an activity

    • guarantees:

    • retry on errors

    • state preservation

    • recovery after failures

    • idempotency of operations

    Temporal allowed us to transform the loan process into an observable state machine, rather than "a script that sometimes crashes".

    ☸️ Infrastructure: Kubernetes

    The entire system was deployed on Kubernetes:

    • each domain module - a separate deployment

    • horizontal scaling:

    • OCR/AI separately

    • risk scoring separately

    • integrations separately

    • fault isolation between services

    🧱 Microservices Architecture

    Each domain became a separate service:

    • Application Service
    • Risk Service
    • Collateral Service
    • Document AI Service
    • Case Assembly Service
    • Deal Service

    Communications:

    • synchronous calls (gRPC/HTTP) for quick verifications
    • asynchronous events via queues for heavy operations

    🔄 How the Process Looks (end-to-end)

    1. A loan application is submitted
    2. Temporal initiates a workflow
    3. In parallel:
    • the client is verified
    • collateral is evaluated
    • AI document processing is initiated
    1. Data flows into Case Assembly
    2. Completeness and consistency are checked
    3. If everything is OK - deal origination is initiated
    4. The transaction is recorded in the bank's systems

    🚀 Key Results

    After transitioning to the new architecture:

    • application processing time significantly decreased
    • process stability increased (no "pipeline crashes")
    • individual stages can be scaled independently
    • implementation of new loan products accelerated
    • full traceability for each loan was achieved
    • manual document processing workload significantly reduced

    💡 Main Architectural Shift

    The most important change was not technological, but conceptual:

    we stopped thinking about a loan as a monolith and started thinking about it as a managed distributed process (workflow), where each step is independent, observable, and recoverable.