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Artificial Intelligence (AI) in Finance

Artificial intelligence is no longer a futuristic concept in financial services. It is here, and it is reshaping the industry at an unprecedented pace.

Financial services businesses are exploring, investing in, and implementing AI at a much faster rate than other industries. From fraud detection and credit risk assessment to personalised financial advice and regulatory compliance, AI is transforming every aspect of finance.

This guide explores what AI in finance means, its key applications, the benefits and risks, implementation steps, and what the future holds.

What Is Artificial Intelligence in Finance?

Artificial intelligence (AI) in finance refers to the use of machine learning, natural language processing (NLP), generative AI, and agentic AI systems to automate, enhance, and transform financial services operations.

Machine Learning (ML) – Algorithms that learn from data to make predictions and decisions without explicit programming. These systems improve over time as they are exposed to more data.

Natural Language Processing (NLP) – Technology that enables computers to understand, interpret, and generate human language. In finance, NLP is used to analyse news, earnings calls, and financial documents.

Generative AI (GenAI) – AI that creates new content, including text, images, and code, based on patterns learned from training data. This includes large language models like GPT that can draft reports, answer questions, and generate insights.

Agentic AI – AI systems capable of autonomous decision-making and executing complex workflows with minimal human intervention. Unlike generative AI, which answers questions or assists employees, agentic AI can carry out parts of a workflow independently.

AI is being used to power both internal operations and customer-facing applications across the front, middle, and back office of financial institutions.

Key Applications of AI in Finance

1. Fraud Detection and Anti-Money Laundering (AML)

AI strengthens fraud detection and improves the security of transactions. It can identify suspicious patterns and anomalies that would be impossible for humans to detect at scale.

AI-powered solutions are being deployed to bolster financial crime detection systems and identify risks at an earlier stage. Banks are using AI to monitor transactions in real time, flagging potential fraud before it occurs.

Real-world impact:

HSBC deployed Google Cloud’s AML AI and cut false positive cases by 60% while detecting two to four times more confirmed suspicious activity than its prior model.

AI-driven AML applications can lead to a 70% false positive reduction while boosting high-risk events detection by 30%.

Agentic AI systems in fraud monitoring can identify an unusual transaction, analyse related signals, and initiate the next permitted action within limits set by the financial institution.

2. Credit Risk Assessment and Underwriting

AI improves credit risk assessment by processing a broader and more diverse set of information than traditional models. About three-quarters of ‘significant’ (larger) banks are using AI and big data technologies for assessing creditworthiness, with another 10% having projects under development.

AI can improve access to credit for businesses with shorter credit histories or fragmented information. Banks that make extensive use of these technologies tend to support the most dynamic businesses more actively.

In practical terms, AI is being used for:

Loan and financial data extraction – Automatically pulling relevant information from loan applications and financial documents

Credit memo drafting – Generating comprehensive credit memos for loan committees

Early warning systems – Detecting early signs of loan deterioration

Automated spreading – Analysing and summarising unprecedented volumes of data such as financial statements

Real-world impact:

Using AI in credit processing decisions can reduce credit approval cycles by up to 60%.

MYbank, a digital bank serving small and micro enterprises, has seen its AI-generated credit limits align with expert assessments in roughly 90% of cases.

AI models that evaluate behavioural patterns instead of static data can improve approval rates while reducing fraud losses.

3. Personalised Financial Advice and Wealth Management

AI enables hyper-personalised wealth management support. Generative AI is being used to offer investment advisory services, helping users plan and manage their overall financing needs.

More than 66% of surveyed Americans who have used generative AI are turning to AI for financial advice. Consumers view banks as the most trusted institutions to safeguard personal data.

Visa AI Financial Assistant allows banks to bring AI-powered financial insights to cardholders under their own brand. It enables cardholders to:

Stay on top of spending with proactive monthly insights

Ask and understand instantly with responses grounded in their own financial activity

Act in the moment by locking a card or setting alerts directly within the conversation

4. Customer Service and Virtual Assistants

AI-powered chatbots and virtual assistants are transforming customer service in banking. AI agents can handle routine queries, freeing human staff for more complex issues.

Agentic AI is being explored for customer service applications, with potential applications across fraud detection, compliance, underwriting, customer service, and personal finance management.

AI assistants can:

Answer customer questions 24/7

Help customers navigate banking products

Provide account information and balances

Assist with loan applications and document submission

Streamline industry research, due diligence, and loan assessment

5. Risk Management and Compliance

AI assists market institutions in ensuring regulatory compliance, including in the field of anti-money laundering. It can help authorities with tasks such as prudential supervision, market surveillance, market abuse monitoring, complaints processing, and sanctions screening.

AI is being used for:

Real-time compliance checks prior to fund settlement

Learning and tracking typical behavioural patterns by customer, product, and corridor

Flagging anomalies such as unusual counterparties

Detecting potential violations of normal customer profiles

6. Research and Investment Analysis

AI is accelerating research and the creation of pitchbooks. Financial professionals often spend 15 to 30 hours per week preparing pitchbooks. AI agents can now build pitchbooks, audit statements, or draft credit memos in minutes.

Real-world applications:

Tiger Brokers saw conversation volumes on its TigerAI tool grow 500% year on year as at June 2026.

Investors use AI tools to digest news, analyse earnings data, and gain portfolio insights.

AI assistants can flag overly bullish sentiment, upcoming earnings reports, and concerns over operating cash flow.

7. Trading and Market Analysis

AI is being used for market analysis, helping traders process, synthesise, and analyse extensive financial reports, relevant data, and historical trends to extract insights more efficiently.

LLM-based agents are capable of complex reasoning, tool use, and autonomous decision-making in investment management. Multi-agent systems are being developed for portfolio management analytics and simulated trading.

8. Banking Operations

AI is transforming core banking operations. State Bank of India has been exploring agentic workflows across risk management, underwriting, personal finance management, customer onboarding, and internal reporting.

In operations, AI can:

Identify low-risk, high-value small business clients by analysing years of customer data

Automate contract reviews, reducing processing time from minutes to seconds

Predict tax treatment of payouts and distinguish between dividend and interest classifications

9. Software Development and IT Operations

According to IBM, the biggest benefit financial institutions get from AI, especially generative AI, is in the software development life cycle (SDLC), where productivity might improve by 20%.

10. Sustainable and Climate Finance

AI, if governed well and applied with precision, can help close the climate finance gap by turning fragmented information into investable opportunities.

The Evolution: From Generative AI to Agentic AI

The AI landscape in finance is rapidly evolving.

2024 was dominated by generative AI. 2025 marked a shift toward more advanced and specialised applications, with agentic AI taking centre stage.

Agentic AI represents the next evolution. Unlike generative AI, which creates content, agentic AI systems are capable of autonomous decision-making and can execute complex workflows.

What makes agentic AI different:

Traditional automation follows predefined rules

Agentic AI allows systems to assess a situation, determine the next step, and carry out a sequence of actions towards a defined outcome

It can coordinate parts of complex processes like loan applications — collecting information, checking it against defined policies, routing documents, and escalating exceptions to an employee

Adoption trends:

As of January 2026, 99% of financial services firms eventually plan to deploy AI agents into production, but only 11% have done so due to implementation challenges.

A recent Deloitte survey found nearly three-quarters of respondents plan to deploy agentic AI within two years, yet only 21% have a mature governance model.

Banks are exploring agentic AI for fraud detection and customer service applications.

Benefits of AI in Finance

1. Improved Efficiency and Productivity

AI automates routine, data-intensive tasks. It reduces the time spent on manual processes, allowing financial professionals to focus on higher-value activities.

15 to 30 hours per week saved on pitchbook preparation

20% productivity improvement in software development

Faster loan processing and underwriting — credit approval cycles reduced by up to 60%

Reduced time spent on compliance and reporting

2. Enhanced Accuracy and Decision-Making

AI processes a broader and more diverse set of information than traditional models, improving the ability to assess credit risk and detect early signs of loan deterioration.

More accurate credit risk assessment

Better fraud detection with fewer false positives

Improved investment analysis and market predictions

Data-driven decision-making with reduced human bias

3. Enhanced Customer Experience

AI enables personalised, responsive, and efficient customer service.

24/7 availability through virtual assistants

Personalised financial advice and recommendations

Faster response times and reduced wait times

Tailored product offerings based on customer behaviour

4. Expanded Access to Financial Services

AI enables financial institutions to serve customers who were previously underserved.

Credit scoring using alternative data

Financial inclusion for unbanked populations

Personalised financial education and guidance

AI-powered due diligence tools helping banks better assess client operations and risks in the absence of physical branches

5. Improved Risk Management

AI enhances the ability to identify, assess, and manage risks.

Real-time risk monitoring

Early warning systems for credit deterioration

Enhanced fraud and AML detection

Better market risk assessment

AI agents continuously scanning portfolios to detect emerging risks using sentiment analysis, news, and sector research

6. Regulatory Compliance

AI helps financial institutions meet increasingly complex regulatory requirements.

Automated compliance monitoring

Real-time regulatory reporting

Enhanced audit trails and transparency

Reduced compliance errors

Risks and Challenges of AI in Finance

1. Algorithmic Bias and Fairness

AI systems can perpetuate or amplify biases present in historical data. AI’s reliance on personal data raises questions around consent and appropriate data governance, not least in respect of the possibility of systemic bias.

2. Lack of Explainability and Transparency

Many AI models are “black boxes” that make it difficult to understand how decisions are made. Issues concerning the opacity of models include the quality, accuracy, and representativeness of the data on which models are trained.

3. Data Privacy and Security

AI systems require vast amounts of data, raising significant privacy and security concerns. Threats to consumer privacy and the risk of AI-led frauds are major concerns.

4. Cybersecurity Vulnerabilities

The adoption of AI could increase the risk of cyberattacks and introduce altogether new sources of cyber risk. It could also increase malicious actors’ capabilities to carry out successful cyberattacks.

5. Hallucinations and Accuracy Issues

Generative AI models can produce “hallucinations” — confident but incorrect outputs. Challenges remain, including AI accuracy, hallucinations, and the need for organisational change.

6. Third-Party Dependency

Intermediaries often rely on third-party providers to acquire innovative technologies. This raises several issues: the need to ensure the robustness of legal and technical arrangements; the risk that many actors become dependent on a limited number of providers, with potential systemic vulnerabilities; the geographic concentration of providers.

7. Implementation Challenges

99% of financial services firms eventually plan to deploy AI agents into production, but only 11% have done so due to implementation challenges.

Challenges include:

Technical debt and skill gaps

Lack of mature governance models — only 21% have one

Organisational change management

AI must be governed by design

8. Job Displacement

AI automation may lead to job losses in certain areas of financial services. JPMorgan Chase CEO Jamie Dimon acknowledged that while technology would improve lives, its negative impacts were also “a legitimate concern”.

9. Regulatory Uncertainty

The regulatory landscape for AI in finance is still evolving. The EU’s AI Act entered into force on 1 August 2024, though most of its requirements will only be applicable starting from 2 August 2026. Some observers fear overregulation.

Implementation Steps for AI in Finance

Step 1: Define Strategy and Objectives

Start by clarifying what you want to achieve with AI and how it aligns with your broader business strategy.

Identify specific business problems AI can solve

Prioritise high-impact, achievable use cases

Define success metrics and KPIs

Assess your current data and technology capabilities

Step 2: Build a Data Foundation

AI is only as good as the data it learns from. A solid data foundation is essential for success.

Assess the quality, completeness, and accessibility of your data

Clean and prepare data for AI applications

Establish data governance and data management practices

Ensure compliance with data protection regulations

Step 3: Develop AI Capabilities

Decide how to build or acquire the AI capabilities you need.

Build in-house AI teams and expertise

Partner with AI vendors and technology providers

Use open-source AI tools and platforms

Consider both proprietary and third-party AI solutions

Step 4: Start with Pilots

Test AI applications in controlled environments before scaling.

Choose low-risk, high-impact use cases for pilots

Initial use cases could include internal document verification and workflows where AI agents coordinate tasks currently passed manually between departments

Validate AI performance and accuracy

Gather feedback from users and stakeholders

Step 5: Implement and Scale

Once pilots are successful, scale AI applications across the organisation.

Integrate AI into existing workflows and processes

Provide training and support for employees

Monitor AI performance and adjust as needed

Implement governance and oversight mechanisms

Step 6: Monitor and Maintain

AI systems require ongoing monitoring and maintenance to remain effective.

Continuously monitor AI performance and accuracy

Update models with new data

Audit AI systems for bias and fairness

Stay current with regulatory developments

The Future of AI in Finance

Agentic AI and Autonomous Finance

The next frontier in AI is agentic AI — systems that can act autonomously on behalf of users. Agentic AI agents could manage complex financial tasks, from tax optimisation and portfolio rebalancing to household budgeting and retirement planning.

Key predictions:

46% expect Agentic AI to drive incremental automation in the next three years

37% anticipate far-reaching transformation of workflows and decision-making

Core banking can be reimagined with agentic AI

Adoption will accelerate once regulators come out with clearer guidelines

Widespread Adoption

AI will become ubiquitous in financial services. By 2028, over 90% of banks plan to incorporate AI in some form.

Current adoption:

About 50% of Japanese financial institutions were already using GenAI, rising to over 70% when including those undertaking trials

45% of respondents in another survey use AI, showing early-phase adoption

Enhanced Personalisation

AI will enable increasingly personalised financial services, with virtual financial assistants providing tailored advice and recommendations based on individual circumstances.

Regulatory Evolution

Regulatory frameworks for AI in finance will continue to evolve. The European AI Act establishes a comprehensive risk-based, legally binding framework to ensure a high level of protection against the possible harmful effects of AI-based techniques.

AI for Financial Inclusion

AI will play a crucial role in expanding financial access to underserved populations. AI-powered credit scoring using alternative data can help bring more people into the formal financial system.

Domain-Specific AI Models

FICO has launched a foundation model tailored for financial services, designed to improve the accuracy and transparency of generative AI applications within the sector. Domain-specific models can lead to a 38% uptick in compliance adherence use cases and a more than 35% increase in world-class transaction analytic models in areas like fraud detection.

How Qeeva Advisory Steps In

At Qeeva Advisory, we understand that implementing AI in financial services can be complex. Many businesses struggle to identify the right applications, manage implementation risks, and ensure regulatory compliance.

Our Advisory Services help you identify the right AI applications for your business, develop an AI strategy, and implement solutions that drive real results.

Need help with financial data? Our Bookkeeping Services ensure your financial records are accurate and complete — the foundation for any AI implementation.

For businesses looking to manage AI-related risks, our Risk Management Services help you identify and mitigate risks associated with AI in finance, including cybersecurity, fraud prevention, and regulatory compliance.

Our Regulatory Compliance and Corporate Compliance & Annual Returns Filing services keep your business in good standing while you innovate with AI.

Our Service Methodology

We don’t do generic. We do thorough, transparent, and actionable.

Step 1: AI Readiness Assessment
We assess your current technology infrastructure, data quality, and organisational readiness for AI adoption.

This step draws on our Advisory Services to identify gaps and opportunities, and our Bookkeeping Services to ensure your financial data is AI-ready.

Step 2: Use Case Identification
We help you identify the highest-impact AI applications for your business.

Our Advisory Services team comes into play here, helping you prioritise use cases based on business value and feasibility.

Step 3: Implementation Planning
We develop a comprehensive implementation plan, including technology selection, data preparation, and change management.

For this, we lean on our Business Strategy Consulting Services to ensure your AI initiatives are grounded in business reality.

Step 4: Risk Management
We help you identify and mitigate risks associated with AI implementation, including cybersecurity, bias, and regulatory compliance.

Our Risk Management Services and Regulatory Compliance support ensure you innovate safely.

Step 5: Ongoing Support & Optimisation
AI isn’t a one-time project. We help you monitor, evaluate, and optimise your AI systems over time.

We keep your financial systems in shape with Bookkeeping Services and ensure you stay compliant with our regulatory support.

Key Takeaways

AI is transforming every aspect of financial services, from fraud detection and credit underwriting to customer service and investment analysis.

The opportunities are significant:

Increased efficiency and productivity

Enhanced accuracy and decision-making

Personalised customer experiences

Reduced operational costs

Expanded access to financial services

Improved risk management

Competitive advantage

But AI also presents important risks:

Algorithmic bias and fairness concerns

Lack of explainability

Data privacy and security risks

Cybersecurity vulnerabilities

Hallucinations and accuracy issues

Third-party dependency

Implementation challenges

Job displacement

Regulatory uncertainty

The key to success is careful planning:

Define clear objectives and priorities

Build a strong data foundation

Start with pilots and scale gradually

Implement robust governance and oversight

Monitor and maintain AI systems

The bottom line: AI is not a luxury in financial services — it is rapidly becoming a necessity. The institutions that adopt AI strategically, manage risks responsibly, and focus on delivering value to customers will thrive in the AI era.

Let’s Talk About Your Business

Implementing AI in finance can feel like a big step. But you don’t have to figure it out alone. At Qeeva Advisory, we’ve helped Nigerian businesses across financial services, insurance, and professional sectors develop and implement AI strategies that deliver real results.

We understand the unique challenges you face — from data quality issues to regulatory complexity and talent shortages. We work alongside you to design and implement AI solutions that fit your business, not the other way around.

Whether you need help with:

Developing an AI strategy aligned with your business objectives

Identifying the right AI applications for your business

Managing AI-related risks including cybersecurity and compliance

Preparing your financial data for AI applications

Implementing and scaling AI solutions

We’re here to support you every step of the way.

The businesses that thrive today are the ones that embrace innovation. Don’t let AI complexity hold you back.

📞 Call us: (+234) 802 320 0801, (+234) 807 576 5799

📧 Email: info@qeeva.com

📍 Visit us: 5, Ishola Bello Close, Off Iyalla Street, Alausa, Ikeja, Lagos, Nigeria

Contact us today to schedule a complimentary consultation. We would love to hear about your business and explore how we can help you leverage AI for financial success.

Your journey to AI-powered finance starts with a conversation. Let’s talk.

Related Services

We offer specialised services to help businesses leverage AI and technology for financial success:

Advisory Services – We provide expert guidance across legal, business, and finance aspects of your business. Our professionals specialise in investments, financials, taxation, business investigations, accountancy, corporate advice, acquisitions, and valuations. We deliver technology-driven solutions to help you stay competitive in an increasingly digital world.

Risk Management Services – Our comprehensive risk management offerings encompass cybersecurity, fraud prevention and mitigation, regulatory compliance, third-party risk management, software security, and internal audit support. We help organisations identify, quantify, and proactively manage credit risk, market risk, liquidity risk, operational risks, and business risks.

Business Strategy Consulting Services – We accelerate business growth with super-growth strategies. Our services encompass business unit strategy, business planning, commercial due diligence, business case formulation, organisational strategy, and pioneering business model innovation.

Market Research Services – We conduct rigorous market research to understand customer behaviour, competitive landscape, regulatory environment, and cultural nuances. Our research covers product research, market research, sales methods and policies, advertising, pricing, distribution, business environment, and corporate responsibility.

Bookkeeping Services – Accurate financial records are the foundation for any AI implementation. We provide day-to-day bookkeeping and accounting, books balanced and reconciled quarterly, annual accounts and tax returns preparation, and financial statement preparation.

Regulatory Compliance – Ensure your business meets all legal and regulatory requirements while building robust risk management systems.