rohit mathur

Winning High-Stakes RFPs-Part 2B/10 Solution Engineering Series : Domain Architecture Blueprints – NBFC & Healthcare (AI × Compliance × Engineering) 🏦 NBFC – Credit, Risk, and Collections 🔹 Before the Loan (Pre-Disbursal): Lead Capture: Integrate CRM, referral systems, or DSA (Direct Selling Agents) into the loan journey Credit Decisioning: AI Scorecard built from: Credit Bureau scores, Banking patterns, Behavioral + Device data XAI (Explainable AI): Use SHAP or LIME to explain to regulators/customers why a loan was rejected Bank Statement Parsing: APIs like Perfios, Finarkein extract income, inflows, and bounce history; ML flags irregular patterns (e.g., sudden income drop) Underwriting Automation: CKYC (Central KYC), CRILC (Large Credit Exposure), MCA (Company data), GST filings auto-verified. CAM (Credit Appraisal Memo) built via GenAI to reduce manual errors 🔹 After the Loan (Post-Disbursal) Mandates & Escrow:API-based eMandates (Enach, BBPS) → Escrow accounts tagged to loans. Auto reconciliation between LMS (Loan Mgmt System), Tally, and Bank Risk Monitoring: DPD (Days Past Due) buckets → 0-30, 30–60, 90+ NPA (Non-performing Assets) & ECL (Expected Credit Loss) as per RBI auto-triggered Collections Engine: Flow-based routing: Soft bounce vs. fraud cases; IVR bots, WhatsApp reminders, Agency apps integrated. Early Warning System (EWS): AI detects stress using: GST trends (monthly), MCA financials (quarterly), Repayment behaviour 🔐 RBI Compliance Controls Role-based vaults, secure consent capture Audit logs mapped to every action RBI-mandated uptime (RTO, RPO), provisioning rules, CKYC/CRILC logs 🏥 Healthcare – Clinical Ops to Recovery (NDHM & HIPAA Compliant) 🔹 In-Hospital EHR Systems (Electronic Health Records): HL7/FHIR APIs connect labs, vitals, prescriptions LLM (Large Language Model) Copilot: Summarizes patient history, detects gaps in diagnosis Alerts comorbidity or medication conflict in real time Consent Vaults: Patients own their data → control access by doctor, insurer, nurse 🔹 After Care Wearable Monitoring: BP, sugar, SpO2 streamed from IoT devices to care teams GenAI Coach: Personalized post-surgery diet, alerts via chatbot Readmission Prediction: LLMs use prior notes + real vitals → alert CMOs early 🔐 NDHM/HIPAA Compliance PHI (Patient Health Info) is row-level masked Secure ledger for each episode visit RTO/RPO governance with zero data leakage 🤖 AI Patterns Applied XAI = explain credit/clinical decisions GenAI = summarize, pre-fill, coach Agentic AI = borrower/patient assistant bots that reason and act MLOps = auto-retrain when models drift Up Next → Part 2C: AI Patterns across Domains: GenAI, Agentic AI, Drift, Risk, and Security Maturity #NBFC #Healthcare #AI #XAI #GenAI #AgenticAI #Compliance #RBI #HIPAA #FHIR #EHR #RFP #SolutionEngineering #CTO #Architecture #RegTech

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Major microstructure changes and rough dates that you should know when backtesting with CME market data: Jan 2, 2013 - Messaging Efficiency Program becomes effective. Jun 3, 2013 - Self-match prevention functionality launched. Mar 9, 2014 - New iLink and MSGW architecture (Phase 1) launched for TAS markets. Sep 28, 2014 - CME introduces MDP 3.0 based on SBE. Sep 21, 2015 - BTIC launched. Jan 31, 2016 - New iLink and MSGW architecture pre-launch for remaining markets (Phase 2). Apr 10, 2016 - New iLink and MSGW architecture in production for remaining markets (Phase 2). Jul 10, 2016 - Major performance enhancements to CME Globex. (?) Jul 17, 2016 - Production rollout of iLink nanosecond timestamp enhancement and tag 5979. May 21, 2017 - Production rollout of MBO for final market segment. Jul 17, 2017 - Russell index futures and options return to CME. Sep 24, 2017 - Implied functionality for FX futures and SD calendar spreads. Dec 18, 2017 - CME launches crypto futures with Bitcoin futures. Mar 25, 2018 - FX Link launched. May 13, 2018 - TACO launched. Jul 29, 2018 - iLink performance enhancements. Jan 13, 2019 - 9-digit price precision extension. Feb 3, 2019 - Launch of consolidated iLink fill messages. May 6, 2019 - Micro E-mini futures launched. Jun 17, 2020 - Production rollout of binary iLink 3.0 for final market segment. Jun 27, 2021 - Major performance enhancements to CME Globex. Jun 27, 2021 - 3:15 - 3:30 PM CT pause eliminated for CME equity index futures and options. Sep 17, 2021 - Standard-sized S&P 500 index futures and options delisted. Apr 14, 2023 - Eurodollar futures and options converted to SOFR contracts. Jun 26, 2023 - Eurodollar futures and options delisted with expiration of June contracts. Jan 7, 2024 - Major performance enhancements to CME Globex. Jan 7, 2024 - Mass quote messaging capacity increased. Mar 25, 2024 - Micro Treasury futures launched. Need a microstructure-aware vendor and market depth going back to 2009? You get both with #Databento.

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𝐇𝐨𝐰 𝐢𝐬 𝐭𝐡𝐞 𝐈𝐭𝐮𝐬 𝐩𝐨𝐫𝐭𝐟𝐨𝐥𝐢𝐨 𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧𝐞𝐝 𝐫𝐢𝐠𝐡𝐭 𝐧𝐨𝐰? In this quick byte, Naveen Chandramohan, CIO at Itus Capital Advisors, breaks down the thinking behind the current portfolio construct. With a revenue and earnings growth delta of over 3% versus the Nifty 500 (as of Q4 FY25), the portfolio is tilted toward segments primed for potential outperformance in the coming two quarters. It’s an approach of disciplined capital allocation aligned with visible earnings power.

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AMPS Messaging Series (Part 1): Lifecycle-Driven Messaging as a Data Pipeline In ETF and fund services platforms, messaging failures are rarely UI problems. They are architecture problems. Unbounded chats, tightly coupled producers, manual routing, and indefinite retention create: • Storage sprawl • Compliance ambiguity • Operational noise • Fragile downstream integrations AMPS Messaging approaches messaging as a lifecycle-governed data pipeline, not a conversation log. This post starts a technical series. Future posts will deep-dive into Kafka-style event streaming and Redis-backed state and filtering, but first—the core model. Core Concept: Producers Publish. Pipelines Govern. Subscribers Plug In. AMPS Messaging is built on a producer–pipeline–subscriber architecture, where lifecycle and filters are first-class citizens. Key Architectural Behaviors 1. Originators Stay Simple Producers only generate events with mandatory context. They do not care about: • Who consumes • How long data lives • Where it is stored This removes upstream complexity permanently. ⸻ 2. Lifecycle Is Enforced Centrally Each message is born with an expiry path: • Active → Archived → Expired → Purged Retention is enforced by policy, not by user behavior. Messages are events with intent, not history to hoard. ⸻ 3. Filters Replace Broadcasts Visibility is determined by: • Fund, process, role, event class • Time relevance • Subscription intent Noise reduction is systemic, not manual. ⸻ 4. Subscribers Are Truly Decoupled Applications can: • Subscribe later • Replay eligible events within retention windows • Be added or removed without touching producers This is what makes the model future-proof. ⸻ 5. Signal vs Record Is Explicit • Messages = transient operational signals • Documents = persistent regulatory records Pipelines manage signals. Virtual libraries preserve records. They are linked—but never conflated. ⸻ Why This Matters (Especially for ETF & Fund Services) This architecture enables: • Controlled communication at scale • Regulatory-aligned retention without over-collection • Faster onboarding of new systems • Clean foundations for automation and observability AMPS Messaging is not chat software. It is lifecycle-aware event infrastructure for institutional finance. ⸻ Coming Next in This Series • Part 2: Kafka-style streaming semantics for AMPS Messaging • Part 3: Redis-backed filtering, state, and fast subscriber resolution • Part 4: Replay windows, audit reconstruction, and compliance views If you build or operate platforms in ETF, fund services, or capital markets, this series will be practical, not theoretical. Follow along. #AMPSMessaging #EventDrivenArchitecture #ETFTechnology #FundServicesTech #Kafka #Redis #RegTech #CapitalMarketsInfrastructure #PlatformEngineering

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In a significant development for India's data intelligence & fraud sector, Data Sutram has secured a funding round of $9 million, co-led by B Capital and Lightspeed Venture Partners. Data Sutram Team continues to architect the infrastructure powering India's digital transformation. This strategic capital represents an alignment with institutions that understand the profound implications of data intelligence at scale. As India's digital economy accelerates, intelligent data processing becomes the defining competitive advantage. Data Sutram's platform provides the infrastructure powering next-generation financial services and enterprise decisions. We’re thrilled to welcome B Capital and Lightspeed aboard and look forward to building the future together and accelerating Data Sutram’s growth & impact. #FundingAnnouncement #DataSutram #BCapital #Lightspeed #SingularityAMC #DataIntelligence #VentureCapital #StartupGrowth

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Data mesh or Data Fabric- Data mesh - a decentralized, domain-driven approach to data management. Data ownership is distributed across teams. Each team manages its own data products - they take ownership, maintain quality and manage costs. 💠better for larger organizations 💠 better for domain-specific/specialized teams 💠 promotes innovation within teams at the rate they are comfortable with 💠requires mature organizational technology (software and data) practices Data Fabric - centralized architecture. The common data team manages the data environment. Centralized governance and control over data.  💠Faster in terms of implementation  💠Can be calibrated based on the technology maturity of the organization  💠Promotes standardization across the organization Many organizations use a mix of both to get the best of both worlds. This hybrid approach is becoming common, especially in industries like finance, where both flexibility and governance are needed. #DataEngineering #DataAnalysis #Architecture #Fabric #DataMesh

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