15 real-life Business Process Automation examples in Financial Services and Capital Markets
15 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
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%.
Retail and SME Banking
People
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
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
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%
