Introducing new tools to the financial sector is tricky because of sensitive data and strict regulations. Generative AI (artificial intelligence), however, offers a practical path forward by boosting productivity, accuracy, and supporting compliance, all without requiring a complete overhaul of existing systems. The key here is knowing where it delivers the most value and how to leverage the technology’s unique capabilities.

Why Use Generative AI in Finance

Generative AI for finance helps teams work faster without compromising quality. Instead of spending hours preparing credit memos, audit summaries, or financial reporting packages, you can use AI-powered tools to create first drafts in minutes. This frees up valuable time for your team, which they can better use for deeper analysis and strategic work over data prep.

A strong example comes from Bronson.AI’s automation work with Allyant, a global provider of accessibility services. After multiple mergers, Allyant was left with fragmented financial systems, creating siloed data, inconsistent reports, and long, error-prone reporting cycles.

To solve this, Bronson.AI built a suite of automated Alteryx workflows to extract, validate, and unify financial data across all its systems. The result was faster reporting, improved data accuracy, and a scalable process that freed up staff from manual reconciliation work. It gave leadership the insight and speed needed to scale finance operations confidently.

Additionally, in a heavily regulated sector, accuracy and control matter. Generative AI can help automate regulatory mapping, compliance reviews, and policy updates by scanning new laws and turning them into easy-to-understand summaries.

It’s also improving risk management. Traditional systems generate too many false alerts, especially in fraud or AML monitoring. AI better distinguishes between legitimate and suspicious transactions. In fact, Commonwealth Bank reported a 50% drop in scam losses after using AI-based chatbots.

GenAI Technologies Powering Finance

If you’re planning to leverage genAI for your financial services, you must understand three main technologies that make it happen. These tools are the foundation of how AI helps with smarter automation and deeper data insights. Learning how each one works and where it fits into your current systems is important for results that don’t waste your time or budget.

Large Language Models (LLMs)

LLMs are the engines behind most generative AI tools in finance. They process and generate text, which means they can read complex rules, write draft documents, and respond to detailed questions.

For example, LLMs can read through regulatory guidelines like Basel III or GDPR and summarize them into simple, clear instructions for your teams. They also generate credit memos, risk reports, and customer responses that used to take hours of manual work.

Finance teams can start by using LLM tools to summarize internal reports or regulations. Look for secure platforms where sensitive financial data stays protected. Key features to prioritize include role-based access controls, data encryption at rest and in transit, audit logs, and on-premise or private cloud deployment options to meet compliance standards.

Generative Adversarial Networks (GANs)

GANs are key to safe and smart model training. These tools create “synthetic” datasets (i.e. fake data that behaves like the real thing) without exposing any private customer information.

In the financial landscape, this is a major breakthrough. Using real customer data to train models carries serious risks. But GANs can help banks test fraud detection or lending models using high-quality data that protects privacy and meets compliance standards.

If your team is building AI models but worried about privacy, consider adding GANs to your workflow. It’s a practical way to keep up with technology innovation while protecting both data and your reputation.

Internal AI Copilots

Think of AI copilots as digital assistants built for your company. They sit inside your secure systems and help employees complete tasks like writing reports, finding key data, or drafting emails.

At OCBC Bank, for example, an internal generative AI tool helped 30,000 employees get work done 50% faster. These copilots save time, cut down repetitive tasks, and give team members more space to focus on strategy.

For companies concerned with budget, this is one of the best ways to get started. You don’t need to overhaul your systems. You just need to integrate copilots into the tools your teams already use, like SharePoint, Teams, or your internal portals.

Strategic Applications of Generative AI in Finance

Financial institutions are already seeing how generative AI can help them compete and deliver value. Knowing where and how to apply it is the key to reaching measurable gains in your business.

1. Internal Productivity & Knowledge Management

Generative AI helps finance teams do more in less time by automating the work that used to take hours. For example, instead of starting every pitchbook from scratch, teams can use generative AI to pull data, build layouts, and suggest content based on past deals and client profiles.

The same goes for credit memos. AI can gather client financials, highlight risk factors, and generate a first draft for review. This speeds up work without cutting corners.

Summarizing internal research and documents is another major win. With generative tools, your teams don’t have to dig through 50-page PDFs or scattered files. AI can read reports, pull out key points, and give a short, clear summary in seconds.

If you’re managing a department or budget, this is a smart first step. These tools don’t replace people. They help your staff get better results, faster.

Start by identifying repeatable, document-heavy tasks. Then, look for AI tools that integrate smoothly with existing workflows to maximize efficiency.

2. Regulatory Compliance & Policy Management

Staying compliant is one of the biggest responsibilities in finance, and one of the most time-consuming. With constant changes in laws like Basel III and regional banking rules, even small mistakes can lead to audits, fines, or lost trust. Generative AI helps fix this by making compliance work faster, clearer, and more accurate.

Instead of reading long rulebooks, generative AI can scan new regulations and match them directly to your internal policies. This makes it easy to see what needs to change, and what’s already in line.

AI also creates summaries of complex legal documents, turning 50 pages of legal text into a 1-page brief your team can actually use. This saves hours every week for analysts and legal teams.

Another benefit is real-time monitoring. As rules evolve, generative tools can alert your team and suggest what procedures should change before problems happen. This is key for risk management and audit prep. You’re not just reacting; you’re staying ahead.

Take, for example, Bronson.AI’s data review for the Bank of Canada. The bank relied on securities data from multiple external sources, which required extensive manual cleanup to be usable for financial market monitoring and research.

To solve this, we developed a series of Alteryx workflows that automated the cleaning and standardization of these records, even when dealing with inconsistent naming conventions and data irregularities. As a result, there’s reduced duplication, improved accuracy, and a scalable, automated system that saves time and supports more reliable internal decision-making.

Using AI also helps reduce human error. Compliance tasks done manually often miss small but important details. AI handles the same task the same way every time, making sure nothing gets skipped.

Organizations that adopt AI-driven reconciliation and data validation tools have reported cutting manual review time by over 75% within the first few months. These improvements make your business audit-ready and free up your team’s time for higher-value work.

3. Fraud Detection & Anti-Money Laundering (AML)

Fraud and money laundering are major threats in finance, so catching them early is critical. However, old systems often flood teams with false alerts, making it hard to spot real problems. Generative AI helps solve this by understanding context and reducing noise, so your team can focus on real risks.

One key use case is writing Suspicious Activity Reports (SARs). Instead of building them from scratch, generative AI can review transactions, flag unusual activity, and draft a complete SAR ready for analyst review. This saves time and improves accuracy.

AI also improves transaction monitoring. Unlike rule-based systems that only flag certain keywords or amounts, generative models can look at patterns over time. For example, it can tell the difference between a real risky transaction and something that just looks strange on paper, which helps lower false positives.

The results speak for themselves. Mastercard doubled its ability to catch compromised cards and cut down on false fraud alerts by up to 200%. GenAI tech also helped the company quickly identify merchants who may be or have been targeted by fraudsters.

4. Credit Risk Assessment

Credit risk assessment is one of the most important and resource-heavy tasks in finance. Every decision depends on accurate, complete data, and that’s where generative AI can make a real difference. It helps finance teams analyze broader information and make lending decisions faster and more consistently.

Generative AI can summarize an applicant’s financial behavior across multiple institutions. Rather than relying only on credit scores or income reports, AI reviews data from transactions, loan histories, and even public records. It then creates a clear summary of the applicant’s financial health. This gives analysts and decision-makers a full picture, not just a single number or overview.

In connection, this reduces bias. Traditional models often rely on limited data that can unintentionally favor or exclude certain applicants. GenAI reduces that bias by analyzing broader datasets, focusing on actual financial behavior instead of assumptions. The result is fairer, more transparent lending.

It also automates parts of the credit decision process. By understanding both structured and unstructured financial data, generative tools can suggest approval, decline, or further review based on predefined policies and historical outcomes. This cuts manual review time, which helps lenders serve more customers without increasing staffing costs.

Lastly, generative models support adaptive, scenario-based stress testing. Using synthetic data generated by AI, financial institutions can simulate “what-if” conditions, like sudden interest rate hikes or market downturns, and see how their loan portfolios would perform. This type of testing strengthens risk management and keeps your organization ready for change.

5. Wealth Management and Client Advisory

Wealth managers can deliver more personalized service with generative tech. Now, you can create tailored investment strategies for each client. AI can look at income, spending habits, goals, and risk tolerance and build a plan that fits all in a few minutes. Instead of using one-size-fits-all models, AI adjusts based on what each person needs.

It also tracks behavior and market changes in real time. If a client’s spending shifts or the market moves, AI tools can recommend updates right away. This helps advisors stay ahead of client needs, not just react after something happens.

If you’re managing a client-facing team, consider starting with AI tools that automate portfolio reviews or client check-ins. These tools deliver quick wins, increase client satisfaction, and prove the ROI of generative AI in just a few weeks.

6. Algorithmic Trading and Market Intelligence

GenAI can process huge volumes of financial data in seconds, including prices, news, earnings reports, and social media sentiment. Due to this, it can find hidden patterns that humans often miss, helping traders make smarter decisions.

Next, it helps build and test custom trading strategies. Instead of relying on old models, traders can use generative tools to create new strategies based on current market behavior. These strategies can then be backtested using past data to see how they would have performed.

It also improves trade execution. Generative models can help choose the best time and method to place trades, reducing costs and slippage. At the same time, they monitor trades to make sure they stay within your risk limits, protecting your capital.

7. Customer Service Automation (Internal and External)

Customer service is a key part of the finance experience, and generative AI makes it faster, smarter, and more personal. It helps teams respond in real time, handle complex questions, and boost customer satisfaction without raising costs.

You can use genAI to create personalized replies instantly. Whether it’s answering a client about account activity or explaining a loan process, AI tools can draft responses that match each customer’s situation.

It also understands the full context when configured correctly. That means it doesn’t just look at the last message. It reviews past conversations, customer data, and financial history. This helps AI give accurate answers to even complex questions, reducing the need to escalate.

The result is faster resolutions and happier customers. When clients get the right answer the first time, trust goes up and support costs go down.

It works the same way for internal service teams, like HR or IT support within a bank. Internal AI copilots help improve service for staff across departments. These tools stay inside your company’s systems and help employees handle routine requests, draft replies, and find key information quickly.

Bank of America has seen the impact at scale. More than 90% of its employees now use an internal AI-powered assistant, which has cut IT service desk call volume by over 50%. This kind of automation frees up thousands of hours and boosts employee productivity across departments. For organizations with large workforces or lean support teams, it’s a clear path to better service at lower cost.

Common Challenges of Generative AI in Finance

Generative AI offers big value, but it’s not plug-and-play. Financial institutions face real challenges when trying to adopt it, from old systems to talent gaps and tough rules. Fortunately, each challenge has a practical solution.

1. Complexity of Implementation

The problem: Many banks and financial firms still run on outdated systems. These legacy platforms weren’t built for modern AI tools and often block smooth integration.

The fix: Start small with internal use cases like report generation or compliance summaries. These are safer, faster to launch, and show early value. Meanwhile, invest in scalable infrastructure, like cloud-based environments, that can support future growth. This makes it easier to add more generative AI solutions over time.

2. Talent Shortages

The problem: There’s still a 35% gap in the need for people with AI skills and talent availability. This slows adoption and limits impact.

The fix: Upskill the people you already have. Focus on training teams in risk management, compliance, and analytics to use AI tools as co-pilots. This means teaching them to review AI output, ask the right questions, and guide model use safely.

For organizations without the bandwidth to train in-house or needing faster results, outsourcing to trusted partners like Bronson.AI can fill critical gaps quickly. We can help you deploy AI for finance effectively, which is faster and more cost-effective than building full teams from scratch.

3. Data Privacy & Security Concerns

The problem: Financial data is sensitive, particularly for personally identifiable information. Using real customer data to train AI models can open the door to privacy risks or compliance issues.

The fix: Use GANs to create synthetic data. This data acts like the real thing but doesn’t contain any personal info. It’s safe for training models and helps you stay compliant. Many firms now use this method to avoid legal and regulatory trouble while still building strong AI tools.

4. Governance & Regulatory Risk

The problem: Generative AI models are complex. They can make decisions in ways even experts don’t fully understand, which raises red flags for regulators. This “black box” problem can lead to failed audits or legal risk.

The fix: Create a centralized AI governance team. Over 50% of top financial firms already do this. It helps keep policies, oversight, and tools consistent across departments.

You should also follow industry frameworks like NIST AI RMF and build a Responsible AI (RAI) program. That means setting rules around fairness, transparency, and accountability, so your AI stays safe, explainable, and trusted.

Modern Finance Needs Smarter Workflows

Finance teams don’t need to reinvent the wheel to use generative AI effectively. The smartest path forward is to identify tedious processes, like manual reporting, data reconciliation, or regulatory reviews, and leverage AI tools to simplify workflows.

Knowing where to start and how to do it safely can be overwhelming. That’s why leading financial institutions partner with Bronson.AI. We help finance teams cut through the complexity and start using genAI where it matters most. Connect with our team today to build your next competitive advantage.