Even today as we speak, AI is massively undertaking major revolutions in the finance industry. With each year passing, there are significant improvements in how machine learning impacts financial outcomes and operations.
By 2030, the global AI in finance market is expected to exceed $190.33 billion from an estimated $38.36 billion in 2024, growing at a compound annual growth rate (CAGR) of 30.6%.
This growth is attributed to the vast diversity of AI applications in finance, and every vertical in the finance industry is deploying its streams in some form or another.
Top investment banks like JP Morgan Chase & Co, Morgan Stanley, and commercial banks such as Wells Fargo, Citigroup, etc actively used AI for various purposes inherent to their financial services model.
This blog will highlight the top AI trends in Finance and what professionals must do to stay ahead of the competition and accommodate suitable applications in their line of work.
Top 6 Applications of AI in Finance - Trends That Are Being Set
The AI market has taken great strides in the financial world of things. Since its dawn at Dartmouth College in 1956. It’s only a matter of time before AI agents and humanoid financier robots come into existence according to the developments going on in that sphere.
As of today, in 2025, AI has been deployed significantly by financial companies and professionals globally to bolster productivity and drive business goals.
Let’s look at the top 6 AI trends/applications in finance that are being used by financial services companies today.
1. AI in Automation – Redefining Efficiency in Finance
According to a KPMG report, 71 percent of companies use AI in finance operations, and 41 percent use it to a moderate or large degree.
The number one factor why AI is most definitely used in every industry is a by-product of the automation it offers. AI automates repetitive tasks like data entry, invoice processing, and a dozen other financial activities while enabling finance professionals to focus on key skills such as strategic decision-making and analysis based on AI-derived data models.
Back in 2023, Goldman Sachs released a report where they predicted that generative AI could replace the equivalent of 300 million full-time jobs globally, with financial services being one of the most impacted sectors.
Two years later we don’t see how AI has replaced jobs but on the contrary, we see how skilled finance professionals use AI to automate routine tasks associated with their work.
AI automates core functions such as:
- Data processing and reporting
- Market analysis
- Customer support
- Regulatory compliance
- Fraud detection
- Loan processing and credit scoring
As Microsoft CEO Satya Nadella comments on AGI, “AI won’t take away human thinking.”
Extending on that idea, it is right to say that AI will help finance professionals and not replace them. It will automate some tasks but create new kinds of work.
Key Example: Banks like JPMorgan Chase, Wells Fargo, and Citibank are leveraging AI automation tools such as UiPath, Automation Anywhere, and Blue Prism to automate repetitive financial tasks. This has resulted in faster transaction processing, reduced operational costs, and improved client services.
How Finance Professionals Can Become Proficient in Automating Tasks Using AI?
- Gain exposure to the AI automation tools and processes used in your niche of financial services
- Stay updated with the latest AI automation trends
2. AI in Asset Management – Reshaping Investment Strategies
Leaders of the Asset Management business like the CEO of JP Morgan Chase & Co, Jamie Dimon, believe that AI will revolutionize how business is done on Wall Street.
David Solomon, CEO of Goldman Sachs commented on how AI is changing processes like drafting IPO filings and analyst research for asset management companies.
He states how gen AI does 95% of the work on IPO prospectus. This is more of a boon than a bane as it helps investment bankers to come up with smarter portfolio strategies based on heavy research compiled by the gen AI model.
Generative AI models like Chat GPT enable top IB firms such as the likes of BlackRock, JP Morgan Chase & Co, Morgan Stanley, Goldman Sachs, etc…
These firms even use their own proprietary AI technologies to evaluate company valuation, compare companies/portfolios, and assess risks. In addition, they also use AI for market analysis, predicting price movements, and improving trade execution.
Key Function of AI in Asset Management - AI helps to formulate portfolio strategies based on personalized individual goals, interests, and risk tolerance levels. They help understand investor behavior and their current financial knowledge to help facilitate asset management in qualitative means.
How Finance Professionals Can Become Proficient in Asset Management Using AI?
Gain exposure to AI tools used in asset management
Compare and check whether AI predictions on portfolio strategies are beneficial based on your knowledge
3. AI in Risk Management - Necessitating Smooth Functioning
A report by McKinsey suggests that AI-powered risk management can reduce financial losses by up to 30%. It’s a major sign of relief for companies as they can maximize profits and grow consistently without suffering from unexpected setbacks.
There’s always a risk factor associated with any business. In the financial spectrum, institutions like banks deploy AI to gain a view of customers’ risk profiles hence combating fraud.
The AI algorithms alert bank managers on specific use-case scenarios such as irregular spending on credit cards possibly averting scammers. Banks are relying on AI-driven risk management to evaluate loan applications with fervent accuracy, with these systems diving deeply into customer transaction histories and saving patterns.
Investors of publicly listed companies in the Stock Market use AI to capture realistic relationships between the macro economy and the financials of a company in events of stressed scenarios. This helps them mitigate risks and invest appropriately.
AI models are also used by financial institutions in matters of regulatory compliance hence enabling their smooth functioning.
Key Example: JPMorgan Chase uses AI models like COiN (Contract Intelligence) to identify risks in legal documents, reducing document review time by 360,000 hours annually.
How Finance Professionals Can Become Proficient in Risk Management Using AI?
- Become adept at interpreting AI-generated risk reports
- Watch out for AI-risk management trends
4. AI in Fraud Detection - Spamming the Spammers Away
AI in fraud detection is expected to reach $108.3 billion by 2033 as a result of increasing cyber threats and data breaches. Financial services firms are now harnessing generative AI to develop chatbots capable of detecting fraud.
With the rise in the potential of gen AI, scammers, and fraudsters are using voice cloning technologies to mimic bank account holders and loot their money.
The U.S. Federal Trade Commission raised concerns about LLMs and other technologies that simulate human behavior via deepfake videos and voice clones.
These issues are now solved by leveraging Retrieval-Augmented Generation (RAG) on the backend while using LLMs to access and answer questions based on policy documents. Information is first retrieved from a knowledge base, improving accuracy by using context to generate responses.
LLM-based assistants running RAG on the backend help mitigate fraud and expedite strategic decision-making. AI is also used by banks and other financial services firms for identity verification.
Deep learning applications combining graph neural networks (GNN), natural language processing (NLP), and computer vision are improving identity verification in know-your-customer (KYC).
Key Example: Mastercard leverages AI algorithms that analyze 75 billion transactions annually, detecting fraudulent behavior faster than traditional methods.
How Finance Professionals Can Become Proficient in Fraud Detection Using AI?
- Develop expertise in AI-based security solutions.
- Stay updated on emerging AI-driven fraud prevention tools.
5. AI in Accounting - Driving Productivity
Today in 2025, accountants are benefiting by using AI tools to automate routine tasks and compile financial reports 5x times faster. According to a Thomson Reuters Institute survey, the Big 4 firms are leading the way in AI adoption and deploying the technology in various verticals of their financial services.
The largest CPA firms are using AI-powered tools to detect anomalies, conduct audits & assurance of financial statements, and build models for budgeting & forecasting.
AI automates the entire aspect of invoice processing and expense management, handling both accounts receivable and payable.
Once the data is imputed into Excel sheets or other interactive dashboards, AI data-predicting tools are used by accountants to assist in business intelligence and decision-making. It’s becoming more and more a part of the accountant’s daily work schedule, AI enabling them to maximize productivity.
AI can also be used by accountants to generate forecast scenarios based on the operating financial ratios of the company’s performance. This allows them to compare between scenarios, check for relevance, and then shoot out appropriate forecast scenarios to the management.
Key Example: EY Helix leverages AI to detect accounting irregularities, improving compliance checks. Deloitte’s AI tool, Argus, analyzes complex financial documents, enhancing audit efficiency.
How Finance Professionals Can Become Proficient in Accounting Using AI?
- Start Using AI tools used for automating accounting processes
- Experiment with different AI tools to feel which is better suited to your workflow.
6. AI in Tax Planning - Optimizing Tax Strategies with Precision
According to taxation firms like EY and PwC, the tax functions have undergone significant transformation with the rise of gen AI. Every tax function such as tax compliance, resolution of tax disputes, management of tax costs, and stakeholder engagement & transparency is performed with the superlative power of LLM models like Chat GPT.
Tax professionals now use Gen AI models to track the latest regulations and policies to ensure tax compliance. GenAI compliance interpreter solutions help tax pros understand and estimate taxes based on the regulatory context.
AI models are also used by tax specialists to identify strategic tax-saving opportunities based on company transactions, operations, and forecasts. Asset values and cash flows are analyzed to compute tax-saving measures and predict future tax liabilities.
One service that’s mostly being revolutionized by AI is tax advisory. Tax professionals use AI tools enabled with tax planning objectives to understand the client's financial position, business operations, and future forecast, and hence derive appropriate tax saving plans.
Key Example: PwC's TaxGPT is an advanced AI tool that analyzes complex tax codes to help businesses make strategic decisions while minimizing risks.
How Finance Professionals Can Become Proficient in Taxation Using AI?
- Master AI taxation tools that help to offer comprehensive tax services
- Update knowledge in multiple AI tools that facilitate qualitative taxation services.
Join the AI Revolution and Become a Finance Pro!
What do you say? Are you ready to bolster productivity and adopt AI tools for specialized financial services? As a growing accountant or financier, you need to master essential AI tools to stay competent in your field of work.
As Andrew Ng, co-founder of Deep Learning and Coursera puts it, “I think the world will just be better if AI is helping us. It will reduce the cost of goods, giving us a good education.”
The future is for those finance professionals who can accommodate change and prudently use AI tools that can take the bulk of repetitive work away while focusing on more important areas like critical decision-making.
At My Logic, we’ll help you crack competitive finance and accounting exams to get started on your path of deploying AI in finance applications.
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