Contents
Why Financial Firms Are Rethinking Operations
A lot of financial companies still rely on slow, manual workflows held together by legacy systems and constant back-and-forth. Whether it’s onboarding clients, handling customer support, approving loans, or closing the books — simple tasks take longer than they should.
And the pressure keeps building.
- Clients want faster responses
- Compliance requirements keep getting stricter
- Margins are tighter. Teams are stretched
At some point, it stops being sustainable.
That’s where automation and AI are starting to make a real difference.
Tasks like document review, data classification, customer queries, or email triaging don’t need to be done by hand anymore. These days, AI can extract, route, and respond — faster and more consistently than a human ever could.
Automating these tasks frees up capacity without increasing headcount. It reduces the risk of human error, speeds up delivery, and helps people spend their time on the things that actually require judgment.
You don’t need a full digital overhaul to get started. Most firms pick one process, automate it, and build from there.
Where Automation Actually Helps: 9 Use Cases
Financial companies are under pressure to move faster, reduce costs, and improve accuracy — all while handling more data than ever. Automation doesn’t just remove busywork; it opens up capacity across core operations.
Here are nine finance-related processes where AI automation is already making a tangible impact:
1. Bookkeeping
Bookkeeping still eats up hours in many financial firms — from reconciling ledgers to matching bank transactions and categorizing expenses. These tasks are repetitive, rules-based, and often delayed due to human bottlenecks.
Example:
JPMorgan Chase built Cash Flow Intelligence, an AI-powered tool that analyzes inflows and outflows, reconciles data across systems, and identifies anomalies without manual input. For many corporate clients, it reduced manual work by 90%.
2. Budgeting
Budget creation is often static, based on outdated assumptions and point-in-time data. When conditions change mid-quarter, budgets rarely reflect it. Automation enables continuous, rolling forecasts that stay aligned with reality.
Example:
Domino’s Pizza worked with JPMorgan to automate forecasting using Cash Flow Intelligence. By integrating real-time cash data into planning, they reduced manual data cleanup and gained faster access to decision-ready numbers.
3. Customer Support
Support teams often deal with repetitive finance-related queries — payment statuses, application updates, basic document requests. These eat up time and delay responses to more complex issues.
Example:
Rocket Mortgage launched Rocket Logic – Synopsis, a generative AI tool that analyzes and summarizes customer calls, detects sentiment, and automates follow-ups. This cut down over 60,000 hours of manual effort per year and resolved 70% of requests without human agents.
4. Data Management
Financial firms sit on troves of unstructured data — client onboarding forms, transaction records, PDFs, internal spreadsheets. Without structure, that data’s useless. AI helps parse, classify, and standardize it at scale.
Example:
Bridgewater Associates uses AI models and AWS tools to process vast sets of investment research data. Their internal AI lab orchestrates specialized agents to handle data extraction, analysis, and reporting, cutting research cycles significantly.
5. Employee Benefit Onboarding
Benefit setup involves repetitive workflows: checking eligibility, enrolling employees, syncing with payroll systems, and generating notifications. Even small delays can create compliance risks or frustration.
6. Expense Management
Expense approvals are often messy: receipts get lost, claims come in late, and reviewing each one takes time. AI tools can scan, classify, and validate expense reports automatically, flagging issues for review.
7. Financial Planning
Scenario modeling and long-term forecasting are essential but tedious. AI speeds up modeling by running multiple simulations based on changing inputs (e.g., cash position, market conditions, revenue projections).
8. Investment Strategies
Advisory firms and asset managers often rely on analyst-driven strategies that take days or weeks to adjust. AI models help firms ingest news, analyze portfolios, and rebalance faster and more accurately.
Example:
Goldman Sachs uses AI to assist in building and optimizing trading strategies. It helps identify emerging market patterns and forecast outcomes faster than traditional quant models alone.
9. Procurement
Vendor onboarding, recurring payment approvals, and invoice matching tend to be low-value but high-volume tasks. Automation helps flag low-risk purchases for auto-approval and catches pricing or contract anomalies early.
What You Actually Get: The Benefits of Automating Financial Workflows
Automation makes finance operations more resilient, less error-prone, and faster to adapt. When repetitive tasks are removed from the day-to-day, teams can move with clarity and focus.
1. Better Decision-Making
Clean data, updated in real time, leads to stronger decisions. Teams spend less time compiling reports and more time understanding what’s driving the numbers.
- Forecasts become more accurate
- Financial reviews happen more frequently
- Leaders gain confidence and act faster
“30–40% of time can be reduced by optimizing financial processes, automation and digitalisation.”
— Route to Finance: 2025
2. Happier Teams
Automation removes the worst parts of the job — repetitive inputs, manual formatting, late-night reconciliations. The work that remains is higher-value and more engaging.
- Less burnout, more strategic work
- Improved retention
- Higher team satisfaction
3. Higher Throughput Without Hiring
Automated workflows process more volume, with fewer errors and less back-and-forth.
- Faster turnarounds
- Easier onboarding of new clients
- Less pressure on stretched teams
4. Fewer Errors, Less Risk
Repetitive manual work leads to avoidable mistakes. Automation reduces that exposure.
- Logic-based rules flag exceptions
- Duplicate entries are caught automatically
- Compliance checks happen in the background
What Gets in the Way: The Challenges of Financial Automation
Even when the benefits are clear, automation can hit friction. These are the most common reasons why projects stall — and how to avoid them.
1. Mistakes Still Happen — Just Faster
Automation doesn’t prevent bad rules or logic. A single misconfiguration can scale errors quickly.
- Build review checkpoints
- Keep logs and alerts in place
- Leave humans in the loop where it matter.
2. Teams Need Time to Reskill
Shifting to automation means roles change — sometimes significantly.
- Train people on systems and logic
- Redefine roles around strategy and oversight
- Support teams during the transition
3. Security and Data Protection
Automation systems need the same (or stronger) safeguards as manual workflows.
- Encrypt data in transit and at rest
- Limit access and run regular audits
- Watch for informal scripts or shadow tools
4. Process Complexity Slows Things Down
Automating a broken process won’t fix it. The best results come when automation is applied to workflows that are already clean and understood.
- Start small
- Simplify before you automate
- Scale once results are clear
Conclusion
Automation works — but trying to automate everything, all at once, doesn’t.
Some processes still need human input. Advisory work, exceptions, nuanced client interactions — these benefit from judgment and flexibility. That’s especially true for smaller firms where adaptability is part of the edge.
The better approach is focused and deliberate:
- Start with low-risk, high-frequency tasks
- Let teams shape how automation fits into their workflow
- Keep manual control where it adds value — not out of habit, but by choice
The goal isn’t to remove people. It’s to remove friction — so your team can work faster, smarter, and with more impact.