AI in Financial Services

McKinsey & Company 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗵𝗼𝘄 𝗯𝗮𝗻𝗸𝘀 𝗰𝗮𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗲𝘅𝘁𝗿𝗮𝗰𝘁 𝗿𝗲𝗮𝗹 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗔𝗜: ⬇️ This is a full-stack, enterprise-grade architecture — built on agents, orchestration, and rewired workflows. The AI bank stack consists out of 4 key layers: ⬇️ 𝟭. 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗟𝗮𝘆𝗲𝗿 This is the user layer — customers and employees. McKinsey calls for fully reimagined, intelligent, personalized experiences across all channels. → Multimodal chat (text, voice, image) → Omnichannel UX across mobile, contact center, branch → Digital twins for customer simulation and workforce training It’s all about a UI refresh and UX overhaul grounded in real AI. 𝟮. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝗸𝗶𝗻𝗴 This is the brain of the AI-first bank. And it’s not just predictive models anymore — it’s orchestrated agent ecosystems. → AI Orchestrators: Plan, reason, delegate across workflows → Domain Agents: Specialize in credit policy, fraud, risk, legal → Copilots: Embedded in workflows to guide users and automate decisions McKinsey reports 20–60% productivity gains in decision-making with this approach. 𝟯. 𝗖𝗼𝗿𝗲 𝗧𝗲𝗰𝗵 & 𝗗𝗮𝘁𝗮 The foundation layer most banks underestimate — until GenAI models stall in production. → Vector databases → LLM orchestration and FinOps → Search and retrieval engines → ML pipelines → Secure data architecture → API infrastructure The goal: make data accessible, tools reusable, and infra invisible to the business. Without this, nothing scales. 𝟰. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗼𝗱𝗲𝗹 This is where the transformation wins or fails. Without rewiring the org, the tech doesn’t matter. → AI control towers to track value and set guardrails → Cross-functional teams across business, tech, and AI → Platform operating model for speed and alignment → Enterprise-wide reuse of AI capabilities If you're building isolated projects without shared assets or central coordination, you’re not transforming — you’re experimenting. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗮𝗹𝗹 𝗮𝗱𝗱𝘀 𝘂𝗽 𝘁𝗼? The banks that win won’t be the ones with the most pilots. They’ll be the ones that industrialize agents, orchestration, and rewired workflows, with full-stack coordination. Full McKinsey article: https://lnkd.in/dPaJzVK4 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

AI agents may face one of their first real stress tests in the office of the CFO. A lot of AI can still be vague and get away with it. Finance can’t. Here, agents are judged less by how fluent they sound, and more by whether the output is accurate, traceable, and usable in real workflows. That’s part of why Sema4.ai is an interesting example here. It’s building at the enterprise agent platform layer, but the value becomes much clearer when that platform gets applied to a specific function like the Office of the CFO. In that setting, the work is very concrete: reconciliation, accounts payable, payment processing, remittance matching, and financial documents. 𝐈𝐭’𝐬 𝐬𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐥𝐚𝐲𝐞𝐫 is trying to make that more workable by letting teams: → query financial data in natural language across databases, spreadsheets, and documents → ground analysis in SQL-powered calculations so outputs are tied to logic that can actually hold up → turn financial documents into structured, queryable data instead of leaving critical information trapped in PDFs and reports That is where this starts to get more interesting. Not just AI that sounds useful in finance, but AI built to operate closer to 𝐭𝐡𝐞 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐬 𝐟𝐢𝐧𝐚𝐧𝐜𝐞 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬. That’s the difference between something that sounds good in theory and something that can actually hold up in a real finance environment. Sema4.ai publicly highlights outcomes such as raising auto-match rates 𝐟𝐫𝐨𝐦 𝟐𝟎% 𝐭𝐨 𝟖𝟎%+, improving invoice processing by up to 4x, and reducing some workflows from 24–48 hours to around 10 minutes. 📍Read more here: https://lnkd.in/gpF7eeaW

We spent a decade building lending apps nobody wanted to use. Then AI picked up the phone. Bajaj Finance just announced their AI voice bots will disburse ₹5,300 crore in FY26. That’s not a pilot—that’s the new playbook. Here’s what everyone is missing: True digital transformation in financial services is NOT about digital journeys with bad UX. It’s about natural language interfaces with AI. The whole app-based, portal-driven self serve digitization wave? It mostly flopped Why are AI voice bots actually working now? Two reasons: India is massively credit-starved (huge demand) + Voice is how Indians actually prefer to transact. For document-heavy loans like LAP, I see voice + WhatsApp bots becoming the killer combo. Voice builds trust. WhatsApp handles documents. The outcome: Lower operational costs → Lower lending rates → Better credit access → Industry efficiency. Bajaj is targeting 90% reduction in service workloads and 50% drop in operations costs by FY30. The lenders who crack natural language interfaces will own the next decade of Indian lending. What’s your take? Are we finally seeing real digital transformation in financial services?

Manual invoice processing costs finance teams hundreds of hours every year? Here's how to fix it. Most finance teams are losing hours every week on AP tasks a system could handle. I spent years as a CFO signing off on payment runs. Half the time, the real bottleneck was upstream - invoice capture, approvals, matching. Not strategy. Data entry. Here is what accounts payable automation actually covers: 𝗜𝗻𝘃𝗼𝗶𝗰𝗲 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 ✅ Automatic invoice capture and data extraction ✅ Email, PDF, portal - all ingested automatically 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 ✅ Approval workflows without chasing people on email ✅ Rules-based routing with a full audit trail 𝗘𝗿𝗿𝗼𝗿 𝗣𝗿𝗲𝘃𝗲𝗻𝘁𝗶𝗼𝗻 ✅ 2-way and 3-way purchase order matching ✅ Duplicate payment detection before it costs you 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 ✅ Real-time view of what is owed and when ❌ No more spreadsheets tracking open invoices manually ❌ No more month-end surprises from unrecorded liabilities 💡 The business case is straightforward. Fewer errors. Faster close. Better cash flow timing. And your team stops doing work that adds zero analytical value. Want to see how AP automation works inside a real finance function? Check out how BILL can transform your process. P.S. Where are you losing the most time and money when it comes to managing AP?

AI is transforming financial services. It’s also transforming financial crime. A recent global analysis reported that banks and insurers are now facing a new wave of 𝐀𝐈-𝐞𝐧𝐚𝐛𝐥𝐞𝐝 𝐟𝐫𝐚𝐮𝐝, 𝐜𝐲𝐛𝐞𝐫𝐚𝐭𝐭𝐚𝐜𝐤𝐬, 𝐚𝐧𝐝 𝐭𝐡𝐢𝐫𝐝-𝐩𝐚𝐫𝐭𝐲 𝐯𝐮𝐥𝐧𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 as they digitize core operations. And the risk curve is steep. Deepfake transactions. Synthetic identities. Model-driven phishing. Automated credential stuffing. Real-time manipulation of underwriting or claims workflows. In parallel, IBM’s 2024 Cost of a Data Breach Report found that 𝐟𝐢𝐧𝐚𝐧𝐜𝐢𝐚𝐥 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐫𝐞𝐦𝐚𝐢𝐧𝐬 𝐨𝐧𝐞 𝐨𝐟 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭-𝐭𝐚𝐫𝐠𝐞𝐭𝐞𝐝 𝐬𝐞𝐜𝐭𝐨𝐫𝐬, with breach costs exceeding 𝐔𝐒𝐃 𝟓.𝟗𝐌 𝐩𝐞𝐫 𝐢𝐧𝐜𝐢𝐝𝐞𝐧𝐭 on average. It implies, AI won’t just accelerate legitimate operations. It will accelerate criminal ones. And this is where leadership matters. Because customers don’t just evaluate financial institutions on product or price. They evaluate them on 𝐭𝐫𝐮𝐬𝐭, the confidence that their data, identity, and money are safe in an increasingly automated world. That’s why AI adoption must move hand-in-hand with: 1. Clear governance frameworks 2. Transparent decision systems 3. Continuous monitoring of model behaviour 4. Strong third-party risk controls 5. Human-in-the-loop safeguards for high-impact decisions AI can make financial systems smarter. But only governance makes them trustworthy. In the next decade, 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 𝐰𝐨𝐧’𝐭 𝐛𝐞 𝐀𝐈 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐀𝐈 𝐢𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲. #FinancialServices #AIGovernance #CyberSecurity

AI Field Note: We just launched the second iteration of PwC's Simplified Audit for Private Business. Here’s what we learned building it. There was a piece doing the rounds last week about how AI still can't reliably read a PDF. One researcher put "PDF parsing is solved" on a joke timeline of AI progress, right before AGI. It resonated because it's true. In audit, PDFs aren't an academic problem. They're part of the job. Every engagement produces hundreds: invoices, bank statements, contracts, leases, handwritten receipts. Some are clean native files. Many are scanned, formatted inconsistently, or stitched together from multiple sources. Extracting structured, reliable data from these documents into testing workbooks is substantive and necessary audit work. We just released Version 2 of Simplified Audit for Private Business. It's an AI-enabled system built for private company audits under AICPA standards. It reads supporting documentation across formats and quality levels, extracts the relevant fields, matches them to testing samples, and produces structured output with source citations for every data point. It covers 25 test types spanning revenue, inventory, fixed assets, accounts receivable, debt, equity, leases, taxes, and operating expenses. The system is built around human judgment, not as a substitute for it. Every output requires an independent, unassisted review. The tool cites its sources so the reviewer can trace each data point back to the original document. It changes the mechanics of the work, but not the professional obligations. We use AI to shift attention from transcription to evaluation. The signal was always there. It just competed with a lot of noise. In high-stakes work, human oversight isn't a temporary control. It's structural. AI changes what professional attention is for (not whether it's needed). Cited, traceable output reviewed by an independent professional is more defensible than either alone. The organizations making real progress with AI are the ones starting from specific, unglamorous problems, building systems that work on actual workloads, and designing for the reality that humans stay in the loop. Reading PDFs and populating structured workbooks for private company audits will never make a keynote. But it's where a remarkable amount of professional work lives, and getting it right is how we raise the standard for all.

Tech Stack of an AI Research Agent: The complete architecture that powers intelligent research automation. Building effective AI research agents requires more than just selecting a good LLM. The real challenge is coordinating multiple specialized components that work together smoothly to deliver accurate and thorough research results. Here's the essential tech stack breakdown: 🔹 1. LLM Backbone drives the core intelligence : GPT-4o excels at multimodal tasks and summarization. Claude 3 handles long-context document analysis very well. Mistral or Llama 3 offer open-source flexibility when you need full control over your deployment. 🔹 2. Memory and Context Management prevent information loss : LangChain or LlamaIndex manage context and effectively handle document chunks. Vector databases like Pinecone, Weaviate, or Chroma store embeddings and allow for semantic search across large document collections. 🔹 3. Web Browsing and Retrieval capabilities gather live information : Search APIs such as Serper, Brave Search, and Bing fetch reliable real-time results. Browser automation tools like Selenium or Playwright scrape dynamic content when static APIs fall short. 🔹 4. Tool Abstractions and Agents coordinate complex workflows : AutoGen enables collaboration among multiple agents. CrewAI provides role-based organization for task-specific responsibilities. LangGraph manages stateful workflows between agents. 🔹 5. Task Routing and Planning handle smart decision-making : Function calling via OpenAI or Claude APIs manages tool selection. ReAct or AutoGPT-style planners support iterative search, analysis, and synthesis processes. 🔹 6. Document Understanding extracts structured information : PDF parsers like Unstructured.io handle content extraction. OCR tools like Tesseract process scanned documents and images. 🔹 7. Output Generation creates professional deliverables : Notion API or Google Docs API generate formatted reports. Whimsical API and Mermaid.js create diagrams and visual summaries. The sample flow showcases the complete cycle: query processing, task breakdown, web search, document parsing, vector storage, summarization, source citation, and final output generation. Success comes from choosing components that integrate well, not just relying on individual tool capabilities. #aiagent

Transforming Financial Institutions Using API-based Approach 💡 External pressures across Financial Services to deliver new customer experiences and product innovation are driving the need for core architecture and systems transformation. APIs are the key ingredient for enabling such a transformation to create a modern, agile financial institution organization. Today, we can notice two-pronged approach to core transformation and platformification: ☁️ Modernize Core Systems via solutions such as resilience-by-design and a shift to the cloud; 🤝 Develop an API network that drives collaboration with ecosystem partners, to enable new products/services and revenue streams. By becoming an ecosystem of business services, change can occur at pace. These business services are then underpinned by IT services which can operate at a Macro level (e.g. SaaS platforms) or at a micro level though the deployment of micro services architectures. APIs enables these services to be decoupled and exchange information through defined and secure contracts. API-led connectivity is based on the principle of connecting systems and exposing data through modern APIs with the integration split into three layers that compliments the different types of APIs: 👨💻 System APIs provide access to the end systems to abstract the complexity of each system. As well as providing downstream insulation, System APIs provide a single point of entry, a single point of governance and management, as well as single, consistent way of accessing the data. 🌐 Process APIs orchestrate data extracted via the System APIs and encapsulate business processes independent of the data source or destination, to create a higher level of value. The orchestration involves one or more aggregating, splitting and routing of data. 📱 Experience APIs are designed specifically for consumption by a specific end-user, an application or a device. This API layer allows developers to quickly innovate or build new experiences by consuming the underlying assets without having to know how the data or the business capability go there. If anything changes to any of the systems or processes, it requires minimal changes to the experience layer and therein lies the agility required by IT to respond rapidly to changes to business requirements. Developing an API based strategy is the key to addressing the challenges and opportunities presented by the rapidly evolving digital environment for financial services organizations. There will be different starting points for the journey depend upon the organization's maturity, however there is a need to get started to ensure one is not left behind as the pace of change is only increasing. Source: Capgemini x MuleSoft - https://bit.ly/44iQNbF #Innovation #Fintech #Banking #OpenBanking #EmbeddedFinance #API #Microservices #FinancialServices #Data #Cloud #SaaS #Ecosystem #OpenEconomy

This #WorkLab article showcases an inspiring example of Microsoft #Copilot in action. Dow partnered with Microsoft to transform its freight invoicing system, uncovering millions in potential savings.    With billions spent annually on shipping, small errors like surcharges and duplicate invoices added up quickly. By leveraging #AI agents powered by Copilot, Dow automated the review of 4,000 daily invoices, flagging anomalies and streamlining global operations. In just weeks, the pilot identified significant savings, and once fully deployed, Dow anticipates reducing freight costs by up to 3%.    By grounding AI in data, Dow is not only cutting costs but also building a foundation for automation across logistics and customer service—showcasing the transformative power of AI in action.