27 real-life Business Process Automation examples in Financial Services and Capital Markets
27 real-life Business Process Automation examples in Financial Services and Capital Markets
Automation in financial services and capital markets is useful because firms in this sector operate under regulatory scrutiny, large transaction volumes, and very low tolerance for errors, latency, or poor auditability.
The existing examples in private equity, wealth management, and banking show that value comes from accelerating sourcing, due diligence, reporting, and administrative work while preserving controls, traceability, and human sign-off on consequential financial decisions.
Business function
Industry
Plaid's Identity Verification
Plaid leverages ML-powered identity verification to automate the bank account authentication process, replacing slow, manual methods with an AI-driven system that analyzes account patterns and metadata to validate ownership instantly.
Use Case
Bank Account Verification
Tools
Internal Tools
Input
Banking credentials, transaction history, and account metadata
Process
AI verifies bank account ownership and validates user identity by analyzing authentication signals and account patterns in real-time
Output
Instant account verification
Outcome
Reduces verification time from days to seconds with a 99.9% accuracy rate
Payments and Fintech
Data
Leonardo AI's Character Re-generation and Visual Refinement
Springbok Agency utilised Leonardo.ai to transform the brand mascots of Ethias, a leading Belgian insurer, from flat 2D cartoons into emotionally resonant, Pixar-style 3D characters. By integrating AI into their creative pipeline, the agency moved production in-house, enabling the rapid generation of high-fidelity assets that would have traditionally required expensive external 3D modelling and rendering.
Use Case
Character Re-generation and Visual Refinement
Tools
Leonoardo AI
Input
Original brand-specific character designs (the "Ethi" mascots), existing brand assets, and custom-curated databases of high-quality training images.
Process
Springbok built a specialised five-tool AI pipeline with Leonardo.ai at the core. The team uploaded original characters and used a high-fidelity blending engine to regenerate them with enhanced textures and emotional depth. They employed an iterative feedback loop, using successful outputs to further train the model, and utilised Leonardo’s Motion features to animate the static assets. This "NextGen Studio" approach allowed for seamless style matching and scene transfers between Photoshop and the AI model.
Output
A diverse library of 3D-style, emotionally expressive assets and animated content, including variations in character poses, wardrobe, and accessories (such as the addition of an "Ethi" family dog for pet insurance promotions).
Outcome
Production time for complex campaigns was reduced from one month to just one week. By eliminating the need for external 3D contractors, the agency cut production costs by 70% while maintaining absolute brand integrity and scaling creative output across B2C, B2B, and corporate channels.
Insurance and Pensions
Marketing
Wise's Currency Exchange Liquidity Management
Wise utilises predictive machine learning to optimise currency exchange timing and liquidity management, allowing for near real-time conversion at the most favourable rates while bypassing the high fees associated with traditional banking infrastructure.
Use Case
Currency Exchange Rate Optimization
Tools
Internal Tools
Input
Real-time market data, currency fluctuations, and global transaction volumes
Process
AI analyzes market volatility and liquidity patterns to predict optimal conversion windows and automatically manages cross-border liquidity to minimize transaction costs
Output
Optimized conversion timing and automated liquidity balancing
Outcome
$2 billion saved by customers annually in fees compared to traditional banks
Payments and Fintech
Data
Accelerated Deal Sourcing Automation in Private Equity and Venture Capital
Submitted by Frederic Kalinke
This AI Automation helps investment teams accelerate their deal sourcing by automatically analyzing company, industry, and performance metrics from pitch decks, enabling the review of 10× more opportunities through significantly faster and more efficient opportunity processing.
Use Case
Deal Sourcing
Tools
PitchBook, Grata
Input
Pitch Decks
Process
Automatically analyse company, industry and metrics
Due Diligence Automation in Private Equity and Venture Capital
Submitted by Frederic Kalinke
This AI Automation helps investment teams streamline due diligence by parsing prospective company documents to identify critical red flags, delivering a significant reduction in review time and increasing overall deal efficiency by 40% to 60%.
Schroders Capital has deployed a proprietary AI tool called "GAiiA" (Generative AI Investment Analyst) to automate the analysis of private equity investments. The system parses vast quantities of unstructured data to produce first drafts of investment memos, which are then refined by human investment professionals.
Use Case
Investment Memorandum Drafting
Tools
Internal Tools
Input
Financial statements, company filings, sell-side research, and news.
Process
The AI screens data across direct and co-investment opportunities, synthesising key information to answer a pre-set series of investment questions.
Output
First drafts of investment committee memoranda and responses to targeted due diligence enquiries.
Outcome
Enabled the team to assist with over 40 investment cases in its first year, significantly accelerating the due diligence timeline.
Asset and Wealth Management
Operations
Portfolio Performance Monitoring in Private Equity and Venture Capital
Submitted by Frederic Kalinke
This AI Automation helps Private Equity and Venture Capital companies streamline their data analysis for current and prospective portfolio companies by extracting and then aggregating data metrics from disparate data sources, saving considerable time and effort.
BlackRock utilises the "Aladdin Wealth" platform to automate the generation of personalised investment proposals. The tool features a "Next Best Action" engine that uses data-driven recommendations to help advisors identify timely, relevant opportunities to engage with clients based on their specific portfolio needs.
Use Case
Proposal Generation & Client Engagement
Tools
Aladdin Wealth
Input
Client portfolio data, house investment views, and market risk analytics
Process
The platform automatically identifies misalignments in a client’s book (e.g. risk breaches or tax-loss harvesting opportunities) and triggers a notification for the advisor.
Output
Tailored investment proposals, automated "What-If" scenarios, and data-driven engagement alerts.
Outcome
Enhanced "wallet share" through automated asset aggregation and increased advisor efficiency in delivering personalised service at scale.
Asset and Wealth Management
Sales
Affirm's Automated Creditworthiness Checks
Affirm utilizes proprietary machine learning underwriting models to evaluate creditworthiness in real-time at the point of sale, analyzing thousands of data points to offer instant loan eligibility and terms that outperform traditional credit scoring systems.
Use Case
Real-time Underwriting
Tools
Internal Tools
Input
Consumer data, purchase context, and credit bureau data
Process
AI evaluates creditworthiness by processing thousands of variables simultaneously to determine loan eligibility and specific terms at the moment of purchase
Output
Instant lending decisions
Outcome
20% higher approval rate than traditional methods while maintaining a low 3% charge-off rate
Payments and Fintech
Data
Mastercard's Transaction Risk Engine
Mastercard utilizes its Decision Intelligence technology to deploy sophisticated neural networks that evaluate transaction risk in real-time, allowing the company to distinguish between legitimate spending and fraudulent activity with unprecedented precision.
Use Case
Transaction Fraud Detection
Tools
Internal Tools
Input
Transaction details, merchant data, and cardholder patterns
Process
AI scores each transaction in real-time using neural networks trained on global payment data to predict fraud probability and intent
Output
Real-time approval or decline decisions
Outcome
Reduced false declines by 50% and prevented approximately $20 billion in annual fraud
Payments and Fintech
Operations
Zurich's Underwriting Assistant
Zurich North America has deployed a generative AI underwriting assistant, developed by InsurTech firm Sixfold, to automate the synthesis of complex commercial submissions. The tool allows underwriters to bypass the "manual hunt" for data by providing a high-fidelity first draft of the underwriting narrative, tailored specifically to the company’s unique risk appetite and internal formatting standards.
Use Case
Underwriting Narrative Automation and Document Summarisation
Tools
Sixfold
Input
Complex commercial insurance submissions, including broker emails, loss runs, risk reports, and exposure data spanning thousands of pages.
Process
The AI platform ingests the insurer’s specific underwriting guidelines and proprietary risk appetite. It then searches, classifies, and synthesises the submitted documents to identify key risk drivers and inconsistencies. Using Retrieval-Augmented Generation (RAG), it extracts relevant signals to generate a structured underwriting narrative that mimics the preferred tone and documentation standards of Zurich’s specialists.
Output
An AI-generated first draft of the underwriting narrative, risk scores (0–5) based on appetite alignment, and concise summaries of exposure and loss history.
Outcome
Underwriters save an average of 2 hours per submission, allowing them to process 80% of submissions with AI assistance during the initial phase. The saved time is reinvested into high-value broker negotiations and strategic decision-making. Following a successful pilot with 16 underwriters, the solution was expanded from four offices to dozens across the US within six months.
Insurance and Pensions
Operations
Allianz's Project Nemo Agentic Claims Settlement
Allianz deploy a multi-agent AI system in Australia to automate the end-to-end processing of low-complexity insurance claims, from coverage verification through to settlement, reducing processing times by 80%.
Use Case
Agentic Insurance Claims Processing
Tools
Internal Tools
Input
Customer-submitted claim details, policy data, and supporting documentation such as weather event records and purchase receipts.
Process
Seven specialised AI agents collaborate to plan, verify coverage, assess compliance, calculate settlement values, and communicate decisions without manual intervention.
Output
An automated claim decision and (where approved) a settlement payment issued directly to the claimant.
Outcome
Allianz achieve an 80% reduction in claim processing and settlement time, significantly improving customer satisfaction and freeing claims adjusters for complex cases.
Insurance and Pensions
Operations
JPMorgan's Coach AI for Wealth Advisor Client Communications
JPMorgan Asset and Wealth Management deploys Coach AI, a generative AI tool that enables private client advisors to retrieve relevant research, market context, and client data up to 95% faster, allowing them to deliver timely, personalised investment communications during periods of market volatility.
Use Case
AI-Assisted Advisor Research Retrieval and Client Outreach
Tools
Coach AI
Input
Client profile data, account holdings, advisor interaction history, internal research, and real-time market signals from JPMorgan's Connect platform.
Process
Coach AI applies large language models to surface the most relevant content and contextual signals from internal systems, anticipating likely client questions and preparing tailored talking points.
Output
Personalised, context-rich briefings that advisors use to conduct proactive client outreach and answer queries with accuracy and speed.
Outcome
AI-enabled advisors projected to expand individual client rosters by up to 50% within five years.
Asset and Wealth Management
Sales
Bank of America's Erica for Employees
Bank of America deployed Erica for Employees, an AI-powered virtual assistant embedded in its internal operations, achieving over 90% workforce adoption and reducing IT support call volumes by more than half — part of a broader $4 billion technology investment driving measurable productivity gains.
Use Case
AI Employee Virtual Assistant for HR and IT Support
Tools
Internal Tools
Input
Employee queries on IT support, HR policies, benefits, processes, and internal knowledge
Process
Uses natural language processing to understand employee queries across HR, IT, and operational topics
Output
Instant, accurate responses to employee HR and IT queries and automated resolution of routine support requests.
Outcome
Over 90% adoption and IT support call volumes reduced by more than 50%.
HSBC replaces its static rule-based transaction monitoring with a cloud-native AI platform built on Google Cloud AML AI, screening over one billion transactions per month to detect money laundering and financial crime with far greater accuracy than legacy systems.
Real-time transaction data from HSBC customers globally, including amounts, counterparties, geographies, and historical behavioural profiles.
Process
Supervised and unsupervised machine learning models analyse transaction patterns at scale on Google Cloud, learning criminal methodologies and adapting to new evasion techniques continuously.
Output
Risk-scored transaction alerts prioritised for compliance team investigation, with suppressed false positives and network-level criminal connection maps via Quantexa.
Outcome
The system detects two to four times more suspicious activity compared with prior methods, while reducing false positives by 60% with considerable savings in annual compliance review costs.
Retail and SME Banking
Operations
Klarna's AI Shopping and Sales Recommendation Agent
Klarna deploy an OpenAI-powered shopping assistant that enables customers to search for products across thousands of merchant partners, compare options, receive personalised recommendations and find the best available price and stock information, all within a single conversational interface.
Use Case
AI-Powered Conversational Shopping and Product Recommendation
Tools
Internal Tools & OpenAI
Input
Customer queries, purchase history, merchant product catalogues across Klarna's network of 675,000+ merchants, real-time price and stock data, cashback availability
Process
OpenAI LLMs power an assistant that searches across Klarna's merchant network to find matching products, compares pricing and availability and performs reverse search to identify merchants accepting Klarna
Output
Personalised product recommendations with real-time price, stock, delivery, and cashback data
Outcome
Significant revenue uplift (38%) as the company scales its commerce network powered by AI-assisted commerce
Payments and Fintech
Sales
Wagestream's AI Customer Support
Wagestream has revolutionised its internal support infrastructure by deploying Gemini models on Google Cloud to automate the resolution of routine employee inquiries. By integrating real-time account data and historical ticket grounding, the system independently manages queries regarding pay dates and balances, allowing human support agents to dedicate their expertise to high-value, complex problem-solving.
Use Case
Automated Customer Support Resolution
Tools
Google Gemini
Input
Customer support queries, employee account data, pay schedules, balance information, and historical ticket data
Process
Gemini models process queries via API integration with account systems to understand and resolve routine issues, while maintaining an automated escalation path for complex cases
Output
An always-on AI support layer capable of handling the majority of routine inquiries and a scalable infrastructure for rapid growth
Outcome
Over 80% of internal inquiries handled by AI, leading to a significant reduction in workload and faster resolution times
Payments and Fintech
Operations
Lloyds Banking Group's AI-Driven Data Quality Automation
Lloyds Banking Group uses Ataccama ONE to automate data quality monitoring, profiling, and anomaly detection across its systems of record. The platform federates data accountability to Business Platform teams and replaces manual data hygiene processes, enabling the bank to move from assumption to assurance over its critical data.
Use Case
Automated Data Quality Monitoring and Governance
Tools
Ataccama ONE
Input
System-of-record data ingested from across the Group's Business Platform teams, including customer, transaction and operational datasets.
Process
Ataccama ONE automatically profiles datasets, detects duplicates, missing values, and inconsistencies and enforces standardised data quality rules across the organisation using AI-based anomaly detection.
Output
Real-time data quality scores, anomaly alerts, audit-ready quality reports, and remediation recommendations surfaced to data stewards and Business Platform teams.
Outcome
The bank transitions from reactive to proactive data quality management, improving trust in data used for regulatory reporting, AI initiatives, and customer-facing services, while reducing manual data stewardship effort.
Retail and SME Banking
Data
Allianz's Incognito Insurance Fraud Detection
Allianz deploy a supervised machine learning system called Incognito to detect fraud in motor and home insurance claims by analysing distortions in images, videos, and documents submitted with applications.
Use Case
Automated Insurance Fraud Detection
Tools
Internal Tools
Input
Claims submissions including photographs, video evidence, scanned documents, and application data across motor and home insurance lines.
Process
A supervised machine learning model analyses visual and documentary artefacts for anomalies and distortions that indicate manipulation or fraudulent staging.
Output
Flagged claims and applications identified as potentially fraudulent, routed for specialist investigator review.
Outcome
Allianz UK have achieved a 29% increase in fraud detection, saving £37.7 million and increasing application fraud detection by 150% against their target.
Vanguard uses generative AI to automatically produce personalised summaries of its top market perspectives for financial advisors, tailoring content by client financial acumen, investing life stage and preferred tone to streamline educational outreach at scale.
Use Case
Automated Personalised Investor Education Content
Tools
Azure
Input
Vanguard market perspective articles and individual client profile data including financial acumen, life stage, and communication preferences.
Process
A generative AI model reads each article and synthesises a tailored summary with accompanying regulatory disclosures, adjusted to match the specific client profile.
Output
Ready-to-send, personalised article summaries with auto-generated compliance disclosures for each client, delivered through advisor workflows.
Outcome
Advisors distribute relevant, compliant educational content to clients in significantly less time, supporting trust-building and investor engagement at scale.
Asset and Wealth Management
Marketing
BlackRock Aladdin Wealth's Auto Commentary for Advisor Client Engagement
BlackRock's Aladdin Wealth platform uses generative AI to transform portfolio analytics, CIO market outlooks, and client investment preferences into concise, personalised commentary that advisors use to deepen client relationships and explain portfolio positions more clearly.
Aladdin risk analytics, firm-level CIO market outlooks, and individual client portfolio holdings and investment preferences.
Process
GenAI assesses hundreds of data points across the three inputs and synthesises them into concise, advisor-ready bullet-point narratives highlighting the most relevant portfolio insights.
Output
Structured, personalised commentary drafts that advisors use as a starting point for client conversations about portfolio positioning and market context.
Outcome
Advisors spend less time on data gathering and narrative construction, enabling more frequent and more substantive client conversations at scale.
Asset and Wealth Management
Sales
S&P Global's M&A engine
S&P Global utilise a combination of Large Language Models to automate the identification of acquisition targets and the synthesis of vast datasets, allowing their strategy teams to evaluate potential deals with significantly higher speed.
Use Case
Automated Target Sourcing
Tools
Internal Tools & Google Gemini
Input
Global corporate datasets, unstructured market news, and financial filings.
Process
AI agents parse millions of data points to identify companies matching specific strategic criteria, while LLM-ready APIs allow for the rapid extraction of "connected insights" across disparate datasets.
Output
Curated lists of high-probability acquisition targets with automated rationale summaries.
Outcome
Faster decision-making cycles and the ability to evaluate a higher volume of deals without increasing headcount.
Asset and Wealth Management
Corporate Strategy
Lemonade's Jim AI Claims Agent
Lemonade deploy Jim, an AI claims agent within their mobile-first insurance platform, to handle over 30% of all incoming claims autonomously, with the fastest cases resolved and paid out within seconds of submission.
Use Case
Autonomous Insurance Claims Resolution
Tools
Internal Tools
Input
Customer-submitted claims via the Lemonade app, including incident descriptions, supporting photos, and policy details.
Process
The Jim AI agent assesses claim validity against policy terms, runs anti-fraud checks using behavioural signals and cross-referencing, and approves or declines claims without human review where confidence thresholds are met.
Output
An instant claim decision with automated payment processing for approved claims, or a flagged case routed to human review.
Outcome
Lemonade handle more than 30% of claims without any human involvement, with some claims paid in under three seconds, enabling a scalable operational model without proportional headcount growth.
Goldman Sachs becomes the first major bank to deploy Cognition Devin, an agentic AI software engineer, to handle complex multi-step coding tasks including legacy code modernisation and test generation across its 12,000-strong developer team.
Use Case
Autonomous Software Development and Code Modernisation
Tools
Cognition Devin
Input
Internal codebase tasks and developer prompts defining the scope of engineering work, including legacy systems requiring migration to modern programming languages.
Process
Devin, an agentic AI from Cognition Labs, autonomously navigates codebases, writes and tests code, and resolves bugs end-to-end with human review of outputs.
Output
Completed code changes, documentation, and test suites submitted for human engineer review and deployment.
Outcome
Goldman Sachs expects Devin to increase developer productivity by three to four times compared with previous AI coding tools, freeing engineers from drudge work such as legacy migration.
Corporate and Investment Banking
IT
Workforce Automation at BCI Using Microsoft 365 Copilot
British Columbia Investment Management Corporation (BCI), one of Canada's largest institutional investors, deployed Microsoft 365 Copilot to automate manual, repetitive tasks across finance, HR, and operations. The program resulted in over 2,300 person-hours saved, a 10–20% productivity boost for 84% of users, and a month of processing time recovered on a single HR survey analysis.
Use Case
Employee productivity automation and administrative task reduction
Tools
Microsoft 365 Copilot
Input
Meeting recordings, employee survey responses, financial documents, emails, and calendar data stored across Microsoft 365 applications
Process
Microsoft 365 Copilot automatically generates meeting notes and summaries, analyzes employee survey comments to extract themes and action items, streamlines internal audit report writing, and automates repetitive administrative workflows across 22 deployed solutions
Output
Auto-generated meeting notes, summarized survey insights, faster audit reports, and reduced manual task burden across technology, finance, and HR teams
Outcome
84% of Copilot users reported 10–20% productivity gains; 2,300+ person-hours saved; internal audit report writing time reduced by 30%; one month of processing time saved on analysis of 8,000 HR survey comments; employee job satisfaction increased by 68%
Retail and SME Banking
Finance
Goldman Sachs' AI Coding Assistant Rollout
Goldman Sachs deploys GitHub Copilot and Google Gemini Code Assist to more than 12,000 developers, achieving productivity gains of 20% and up to 55% on specific coding tasks. The rollout is integrated into the firm's centralised GS AI platform, where all generated code passes the same quality checks as manually written code.
Use Case
Enterprise-Scale AI Code Assistance
Tools
GitHub Copilot & Google Gemini Code Assist
Input
Developer prompts, existing codebase context, and task descriptions entered by engineers across Goldman Sachs' software development lifecycle.
Process
GitHub Copilot and Gemini Code Assist analyse codebase context and provide real-time code suggestions, completions and refactoring support, integrated within the firm's GS AI internal platform.
Output
AI-generated and completed code suggestions surfaced inline within developer IDEs, passing through the firm's standard automated quality and compliance checks.
Outcome
Developer productivity improves by an average of 20%, with gains of up to 55% on specific tasks, enabling the firm's 12,000-plus engineers to redirect capacity towards complex architecture and higher-value work.
Corporate and Investment Banking
IT
American Express's Gen X Real-Time Fraud Model
American Express deploy their tenth-generation machine learning fraud model, Gen X, to evaluate every credit card transaction in real time across more than 8 billion annual transactions, maintaining the lowest fraud rates in the industry.
Use Case
Real-Time Transaction Fraud Scoring
Tools
NVIDIA AI Platform
Input
Every American Express transaction globally (over 8 billion per year) along with cardholder history, merchant data, and contextual behavioural signals.
Process
A deep learning model executes a sequence of more than 1,000 decision trees, generating a fraud decision within two milliseconds.
Output
A real-time fraud risk score for each transaction, triggering automated approval, denial, or pending review.
Outcome
American Express maintain fraud rates approximately half those of their competitors, protecting over $1.2 trillion in annual card spend.
