Agentify your BFSI enterprise with a Decision Execution Layer
BFSI runs on regulated decisions. We sit between your Systems of Record core banking, loan origination, policy admin, claims, CRM, risk engines, AML/KYC, fraud detection, wealth platforms and payments and your agents, so every onboarding, underwriting, lending, claims, servicing and compliance decision carries context, governance, explainability and memory.
Platform-agnostic approach ensures the best solution for your requirements.
BFSI is full of agents in silos. We give them shared, auditable decision memory.
Credit agents on the loan origination system. Servicing bots on the core banking platform. Claims triage on the policy admin system. KYC and AML case reviewers on the compliance stack. Fraud models flagging payment transactions. All making decisions without a shared Context Object no entities, no state, no record of which decisions worked or why. The Decision Execution Layer is the reasoning tier that sits between your core banking, loan origination, policy admin, claims, CRM, risk engines, AML/KYC, fraud detection, wealth platforms and payments and the vertical agents that act on top. A Context Repo with data, governance rules and knowledge graphs that turns every onboarding, underwriting, claim and transaction into explainable, regulator-ready feedback for the next decision.
Risk-aware pricing and underwriting across loan origination and core banking
Shared Context Repo across core banking, CRM and policy admin
Hyper-personalised next-best-action with suitability and outcome tracking
Governed claims, servicing and complaint handling with full audit trails
Map, Build, Compound
Map
We map the BFSI stack core banking, loan origination, policy admin, claims, CRM, risk engines, AML/KYC, fraud detection, wealth platforms and payments and the Systems of Knowledge (Notion, Slack, Confluence, policy and procedure libraries) around them. We pinpoint where decisions are made today, where Context Objects are missing, where audit trails break, and where outcomes never flow back.
Build
We build the Decision Execution Layer the Context Repo, knowledge graphs and governance rules then wire vertical agents (underwriting, claims, KYC/AML, fraud, servicing, wealth) and general agents on top. Every Context Object carries entities, state, decisions, outcomes and the reasoning trace needed for audit and explainability.
Compound
Outcomes feed back. Credit decisions inform next quarter's risk models. Claim outcomes tune triage. Fraud confirmations and false positives sharpen detection. Regulator findings and conduct reviews harden governance. The Decision Moat deepens with every onboarding, underwriting call and transaction.
Why lemongrass.dev for BFSI
BFSI is full of fragmented systems, legacy cores and shallow AI pilots that can never leave the sandbox because they cannot be explained or audited. You need a partner who treats the enterprise as an operating system and inserts the decision layer that makes agents informed, governed, explainable and compounding.
We align with the metrics BFSI boards track: cost-to-serve, approval and loss rates, time-to-decision, NPS and churn, false-positive rates on fraud and AML, claims leakage, and regulator-ready auditability across every decision.
We wrap your existing core banking, loan origination, policy admin, claims, CRM, risk, AML/KYC, fraud, wealth and payments integrations SaaS and legacy cores stay, but get re-bundled around agentic, explainable use cases.
Guardrails, policy rules and regulatory controls are baked into the execution layer, not bolted onto individual agents. Governance, suitability, conduct and model-risk rules are first-class citizens inside the Context Repo every agent call is logged, reasoned and auditable.
Map, Build, Compound from decision-layer consulting through agent development, Context & Knowledge Graphs, and agent onboarding into live onboarding, underwriting, claims, fraud, AML and servicing ops.
Pre-AI vs agentified BFSI
Onboarding: generic KYC forms and static account opening become prioritised, personalised customer journeys drawn from Context Repo history with AML, sanctions and suitability checks enforced inline.
Underwriting and pricing: static, rule-based score-cards become dynamic, risk-aware decisions that learn when to approve, price for risk or decline with every decision explainable to customer, auditor and regulator.
Lending and transactions: a simple disbursement or payment on the core and loan origination system becomes a logged decision entity, state, decision, outcome, reasoning trace feeding the next one.
Collections and restructuring: hardcoded policy in the core and collections system becomes optimisation for customer lifetime value, forbearance fairness and churn risk with conduct rules enforced.
Claims and servicing: static responses become continuous improvement via outcome tracking across claims, servicing and complaint platforms low-value issues handled in-brand, high-risk and vulnerable-customer cases escalated with the full Context Object.
The Decision Moat: every onboarding, underwriting call, claim and fraud decision becomes a compounding, auditable advantage competitors and challengers cannot replicate.
Near-term agentic use cases for BFSI
Risk-Aware Underwriting & Pricing
Credit and pricing agents on top of the loan origination system and core banking reason over the Context Repo bureau data, customer history, exposure, behavioural signals and past outcomes and decide when to approve, price for risk or decline.
Credit and pricing decisions become dynamic and risk-aware across retail lending, cards, SME and mortgage books. Every call is logged with a reasoning trace so the next one is sharper and fully auditable.
Hyper-Personalised Next-Best-Action
Deposit, card, lending, wealth and insurance offers not only what to offer but when, and whether it is suitable. Agents read the Context Object across core banking, CRM and wealth platforms and trigger offers at the moment most likely to convert, with conduct and suitability guardrails enforced.
Relationship-manager copilots, digital journeys and outbound campaigns share one Context Repo. Hits, misses and suitability outcomes feed back into the decision memory.
Agentic Customer Servicing
Customer queries across website chat, WhatsApp, email, app and phone balance and statement questions, dispute handling, loan servicing, policy endorsements handled by brand-consistent agents drawing on the Context Repo for accounts, products and interaction history. Complex, vulnerable-customer or high-value cases escalate to a human with the full Context Object attached.
Every ticket becomes a logged, auditable decision what was asked, what was offered, what worked sharpening the next response and standing up to complaint review.
Fraud, AML & KYC Monitoring
Agents watch the full customer and transaction journey payments, card activity, onboarding checks, sanctions hits, case-management notes and complaint signals and flag friction, fraud and financial-crime risk early. Cases are routed to the right fraud, AML or conduct team with the full Context Object, reasoning trace and evidence pack attached.
Financial-crime and conduct risk becomes a live signal on the operating floor, not a quarterly regulatory post-mortem with false positives and confirmed cases feeding back into the Context Repo.
Compliance & Conduct Monitoring
Agents scan complaints channels, social, ombudsman signals, call transcripts, adviser notes and policy updates around the clock. Sentiment, recurring themes, conduct breaches and product-level issues land in the Context Repo with the customer, product and account linked in so remediation can start before the next regulator letter arrives.
Conduct and compliance health moves from a quarterly dashboard to a live, account-level signal agents can act on with full evidence trail for supervisors.
AI-Led Operations
Claims, collections, onboarding, middle-office and back-office ops run off a shared Context Repo. Agents sequence tasks based on SLA risk, exposure, vulnerability flags and regulatory deadlines humans approve edge cases, agents handle the rest. Every completed task feeds back into the next day's plan with a full audit trail.
Daily operations stop being run from spreadsheets, email queues and shift handovers and start compounding, with four-eyes controls and audit evidence built in.
Decision layers, proven in production
How BFSI and enterprise teams put the Decision Execution Layer between Systems of Record and their agent fleet and what compounded after.
Let’s map your Decision Execution Layer
Tell us which BFSI systems your agents touch core banking, loan origination, policy admin, claims, CRM, risk engines, AML/KYC, fraud detection, wealth platforms, payments. We’ll map the decision points, design the layer with governance and auditability baked in, and start compounding.