Private LLMs for lending: The cost bottleneck

“As AI-native architectures are redefining finance with smarter personalization, stronger fraud prevention, and resilient APIs. Enterprises are now looking for partners who can build platforms that evolve with their business,” says Rohit Yadava, COO, Aziro (formerly MSys Technologies) https://lnkd.in/d8_K45HS #fintech #india #ai #tech #finance #interviews

Stealthy Workflow Manipulation & Data Exfiltration Once inside your AI automation platform, an attacker can discreetly alter existing flows: Insert a “Document Processor” node to forward every legal brief to an attacker-controlled server. Modify webhook flows to capture inbound client emails for later exfiltration. Inject FTP nodes into RAG pipelines to siphon documents uploaded to Google Drive. Because these changes blend into ordinary workflows, they evade detection unless you have automated integrity checks and a formal code-review process for every flow. See the real-world examples and detection strategies: https://lnkd.in/eyawUW4D #InsiderThreat #Automation #AIsecurity #ThreatHunting #SupplyChainSecurity

AI adoption only becomes meaningful when it changes how critical work gets done. For banks, that means faster and more secure onboarding. For insurers, compliant filings. For governments, services that connect across departments.   Kyndryl’s enhanced Agentic AI Framework, designed with Kyndryl Vital and informed by insights from Kyndryl Bridge; delivers on this by defining human–agent roles, generating compliant agents with our builder, and embedding them into workflows that matter.   The approach defines clear human–agent roles and compresses time to value. Examples include AI-enabled actuarial filings that generate compliant submissions, multi-department government services that connect tax, licensing and benefits, and banking onboarding flows that cut manual steps while improving verification. A quarter of recent signings include AI-related work, reflecting the shift from pilots to production. Details here: https://lnkd.in/g8waBtTD #TheHeartofProgress

WhatsLoan-071 Bharat Series Oct 2025 AI Pilots to quick production 🧩 1. The Atlan Model: “AI Needs Context, Not Just Data”purpose and governance .. Atlan isn’t just aan AI company ; it’s a data collaboration and governance layer that prepares, contextualizes, and manages enterprise data so that AI and analytics can actually work in the real world. Core Functions: • Data unification: Stitch data from 100s of silos — CRM, CBS, LOS/LMS, bureau APIs, etc. • Metadata and lineage: Track where data came from, how it was used, and by whom. • Data governance: Apply policies for access, privacy, and quality (critical under DPDP/RBI). • Context graph: Build relationships between entities (e.g., borrower ↔ loan ↔ property ↔ repayment pattern). • Collaboration: Provide a shared workspace for data scientists, engineers, and business users. So Atlan’s “AI Bridge” is not another chatbot or model — it’s the plumbing that ensures AI gets clean, contextual, and compliant data. 💳 2. Fintech & Lending Use Cases: From Pilot to Production In BFSI, many institutions have AI pilots stuck in silos — for credit scoring, fraud analytics, collections, or lead prioritization — but can’t scale them because of fragmented, ungoverned data. That’s where an Atlan-type layer becomes essential: Platform like Atlan would help BFSI players bridge the “data trust gap” — ensuring every dataset is tagged with purpose, meaning, and governance before AI acts on it. 🌐 4. Potential for Such AI Business Models in BFSI a. B2B Data context Fabric Platforms • Sell to banks ,lenders , BFSI provider networks, and fintechs. • Enable AI-readiness for lending, , risk , fraud assessment / control , underwriting, collections, and KYC systems. b. RegTech + DPDP Compliance-as-a-Service • Manage consent, lineage, and access control for AI models. • Tie into RBI Digital Lending and DPDP frameworks. • Monetize via subscription or per-API transaction model. c. AI Ops for Lending • Provide dashboards that monitor data drift, bias, or governance failures. • Enable “Explainable Credit AI” — audit-ready scoring for regulators. d. Ecosystem Integrator • Bridge data between DPIs like Agristack , OCEN, Finternet, FinID, DigiLocker. • Provide semantic data layer for “Unified Lending Interface (ULI)” workflows. 🚀 5. Future Potential for India India’s BFSI sector has plenty of data but low context. The next wave — especially with Finternet, FinID, and DPDP Act — will require: • Data traceability • Consent tokens • Contextual AI readiness Atlan-type data governance startups can: • Be TSPs (Technology Service Providers) under RBI frameworks. • Enable AI explainability and trust for large-scale credit, investment , insurance, and fraud models. • Power Digital FinID or Digital Public Infrastructure (DPI)-grade data flows. 🔑 Takeaway: In fintech, the next big moat isn’t AI — it’s the context layer that makes AI data meaningful and compliant. WhatsLoan NeoLending Platform

AI agents will drive a shift from basic uptime and bandwidth to a focus on data quality, service resilience, and end-to-end assurance.

Applied AI = Orchestration, not reinvention. With strong base models and MCP-style tool bridges, most apps are “just” wiring the right model to the right tools—and making it reliable. What actually matters • Task → Model fit: GPT/VLM/SLM/LAM based on generation, vision, on-device, or actions. • Grounding & memory: RAG, business rules, per-user context. • Actions via tools: MCP tools to call APIs, DBs, ticketing, email, etc. • Guardrails: authZ, PII redaction, tool allowlists, rate limits, policy prompts. • Evals & QoS: golden sets, offline/online evals, latency & cost budgets, caching. • Observability: traces, prompt/version control, drift playbooks. • Compliance: audit logs, data residency, SOC2/PCI as needed. Minimal stack UI → Orchestrator/Agent → MCP Tools + Retrieval → Models → Guardrails + Evals + Telemetry Payments example (issuer-side) Parse ISO-8583 → RAG to policy/rules → call HSM via MCP tool (ARQC/ARPC) → score risk → respond → log for PCI/audit. Bottom line: The model is table stakes. Grounding, actions, guardrails, and evals are where real products are made. What tools + models are you wiring together this week? #AI #LLM #Agents #MCP #RAG #VectorSearch #AIOps #MLOps #FinTech #Security

🚦 Beyond Top-K and Top-P: Guiding LLMs to Follow a Path and Drive Results : When people think about controlling an LLM, the focus usually lands on the dials — Temperature, Top-K, and Top-P. They’re useful, yes. They shape how creative or safe an answer feels. But if the goal is to make AI follow a path and consistently deliver outcomes, those dials alone aren’t enough. To really harness LLMs, we need to think about the whole system — the prompts we design, the guardrails we enforce, and the feedback loops that keep the model aligned. That’s where control shifts from style of output to direction of results. ⸻ 🔹 1. Decoding & Generation Controls The classic output levers: • Temperature → creativity vs determinism • Top-K & Top-P → diversity of choices • Max tokens → cap output length • Repetition / presence penalties → reduce loops, add freshness • Beam or contrastive search → explore multiple continuations, pick the strongest 💼 Example: In a banking chatbot, you’d keep Temperature low and add presence penalties to ensure repayment-plan options are consistent, not random. ⸻ 🔹 2. Input & Context Controls Shaping the stage before the model speaks: • System prompts → set tone and role boundaries • Few-shot examples → guide reasoning and structure • Retrieval (RAG) → ground responses in external data • Context management → keep conversations focused 💼 Example: For customer service, retrieving the customer’s last 3 transactions ensures the model doesn’t give generic answers but speaks to their actual account activity. ⸻ 🔹 3. Guardrails & Structured Generation Ensuring outputs are safe and usable: • Constrained decoding → enforce JSON, SQL, or other formats • Guardrail frameworks → block unsafe or irrelevant outputs • Function calling → route tasks to tools or APIs • Multi-agent orchestration → planner → executor → verifier loops 💼 Example: When generating loan eligibility decisions, structured JSON ensures downstream risk models receive valid, auditable inputs. ⸻ 🔹 4. Evaluation & Feedback Loops Closing the loop with checks and balances: • Evaluator / critic models → review accuracy, compliance, tone • Re-ranking → select the best from multiple options • Human-in-the-loop → approvals for high-stakes cases 💼 Example: In credit limit increases, an evaluator agent flags cases above a certain threshold for human review before approval. ⸻ 💡 Takeaway Turning the Temperature up or down is not real control. Real control comes from building a pathway — combining prompts, decoding, guardrails, and evaluation — so the model doesn’t just generate text but consistently drives results with confidence.

AI agents are fundamentally changing what we need from the internet—and it's creating challenges none of us originally designed for. We've spent decades optimizing for human users—measuring bandwidth, speed, uptime. But AI agents don't browse websites or stream videos. They make thousands of API calls per second, dynamically choosing paths we never designed for, creating dependency webs that would make your architecture diagrams weep. The real challenge isn't "is the network up?" anymore. It's "is the data trustworthy, fresh, and validated across dozens of sources when an AI agent needs to make a million-dollar decision in milliseconds?" New reality: Your fraud detection AI might touch 50 different services to validate one transaction. One corrupted data feed, one slow API, one quota limit—and the whole chain breaks. We need new metrics for this new internet. Not 99.9% uptime, but "decision-ready data" SLAs. #ThousandEyes #AgenticAI #Infrastructure #TheNewStack #DigitalTransformation https://lnkd.in/gvi6vEyb

Discover how Snowflake's Cortex AI suite addresses data security, regulatory compliance, and AI capabilities for financial services firms seeking advanced AI-driven insights. #AIFinance #AIBanking #Banking #FinTech

The 30% of AI deployments in financial services that posted ROI all had this 1 thing in common: specialized third-party data tailored to the AI or agent's use case. CB Insights' intelligence and analysis of 100+ firms across banking, insurance, and payments revealed this and a few other findings that struck me: 1) Microsoft and OpenAI together make up 1/3 of AI deployments in financial services. 2) Enterprise-wide AI platforms for employee daily use are table stakes. 24% of applications involve these kinds of deployments: -J.P. Morgan supports 200,000+ employees through AWS. -BBVA deployed 3,000 ChatGPT licenses. -UBS reached 30,000 employees. We've moved beyond experimentation to core infrastructure. 3) Production-scale use cases are growing, primarily in customer care. -Truist handled 1M+ conversations via AI in Q1 2025. -ING resolved 20%+ more customer inquiries within seven weeks. -Wells Fargo is targeting 100M annual interactions. 4) Going back to those 30% of deployments that generated quantitative ROI: -AIG with underwriting data. -J.P. Morgan with embedded lending intelligence. -Mastercard doubling fraud detection rates. Skating to the puck: The firms that embed unique relevant data into their AI are creating defensible advantages. See the comments for a link to our team's full report on AI in financial services.