From rule based systems to advanced deep learning models, AI has changed the role of technology across various industries. From healthcare and manufacturing to finance.
Have you ever got a call from your bank about purchases that you wouldn’t usually make? AI fraud detection systems can alert your bank when some thing breaks from your buying pattern. Allowing banks to prevent cyber attacks and other criminal activities.
Amongst other applications of AI in finance there’s customer service, risk management, credit assessments and high-frequency trading. AI-powered systems are streamlining operations, saving time, and improving productivity. Financial institutions and fintech firms are using AI to manage risks, handle routine tasks, and stay competitive.
How AI is Being Used in Finance
According to a Bank of England survey, 75% of firms are already using AI, with another 10% planning to adopt it within three years. The insurance and international banking sectors are at the forefront. About 95% of insurance firms and 94% of international banks use AI in some way.
Despite this widespread adoption, AI application is not without its challenges. The biggest concerns are safety, security, and the reliability of AI models. There’s also a shortage of skilled professionals who can build and manage AI systems effectively.
Challenges
Like many applications of AI the chief challenges to look for are transparency, trustworthiness, explainability, fairness and accountability. Data privacy and security are additional challenges for AI applications in finance.
Transparency & Trustworthiness
The challenge of transparency comes when the decision making process of an AI system is not comprehensive. Users are less likely to trust a system if they don’t understand how it reached a conclusion. To resolve this issue, researchers focus on increasing explainability in AI models.
81% of financial firms use an explainability method, feature importance or Shapley additive explanations. The feature importance method is a quick and general ranking, but doesn’t consider interactions between features. The Shapely values method comes from game theory, assessing all possible feature combinations, but its more computationally heavy. To build trust with regulators, clients and investors regular audits, and a commitment to improving models, will be necessary.
Fairness
Fairness remains a central challenge when implementing AI in finance. AI system reflect and amplify the biases present in their training data. Addressing fairness requires careful attention to data quality, diverse representation, and ongoing bias monitoring. By implementing robust testing and calibration methods, financial institutions can prevent biases.
Accountability
Who’s responsible for AI output? Accountability in AI means clearly defining responsibility for decisions made by automated systems. If AI makes critical decisions based on biased or inaccurate data, it’s difficult to determine responsibility.
Furthermore, the rapid adoption of AI in financial processes amplifies the urgency of addressing accountability issues. As AI systems become more integrated, potential to impact organisations grows. Without clear oversight, errors and biases can go unchecked. Leading to unfair outcomes and legal challenges.
Data Privacy
Data privacy is a big challenge for AI in finance. AI systems often process ensitive personal and financial data. So the power of AI analytics needs to balanced with adherence to data protection regulations. Safeguarding data involves implementing strong encryption, access controls, and anonymisation techniques.
Transparent data usage policies and regular audits further help build customer confidence. By prioritising data privacy, financial organisations not only comply with legal requirements but also protect their reputation.
Security
Security is a cornerstone challenge as financial institutions integrate AI into their operations. With cyber threats becoming more sophisticated, protecting AI systems requires robust cybersecurity measures. Including advanced encryption, continuous system monitoring, and regular security audits to identify weak points.
Skill shortages
Many AI systems require specialised programming, data analytics and machine learning knowledge. Without this knowledge there is a lack of expertise in financial organisations. Plus rapid pace of advancements in AI technologies, adding to the challenge for employees. Investment in training for employees to stay up to date with the necessary skills to use AI systems.
Jobs roles will change with the adoption of AI, which could automate some jobs and augment other roles.
Applications
Why is the finance industry the ideal environment for AI. AI relies on data which the finance industry accrues vast amounts of everyday. Such as transaction data, customer information and market trends. Perfect for training and building predictive AI models and AI applications.
- Fraud Detection – AI can quickly analyse large amounts of financial data. It helps find unusual activity, which prevents fraud and improves security.
- Customer Service Automation – AI-powered chatbots and virtual assistants handle routine customer queries 24/7. Improving customer service while freeing up team members for more complex tasks. AI can improve efficiency of customer service teams, transcribing phone calls and summarising documents.
- Task and Time Management – AI streamlines day-to-day operations. Helping financial teams focus on high-value work and reducing time wasted on repetitive tasks. AI processes information faster than humans. It identifies patterns quickly, allowing for faster insights for decision-making.
- Market Analysis – AI analyses financial markets in real-time, helping investors make smarter decisions.
- Automated Reports – AI models generate financial reports quickly and accurately, reducing workload and improving productivity.
- Accuracy – AI can automate standardised tasks following the same processes, helping reduce manual errors.
The Impact of AI on Financial Institutions
AI is helping financial institutions increase productivity and manage resources more effectively. By automating routine processes, AI reduces human error and allows professionals to focus on decision-making and strategic planning.
However, not all firms fully understand the AI tools they use. 46% of businesses say they have only a partial understanding of their AI technologies.
Only 34% say they fully understand them. Additionally, 55% of AI applications involve some level of automated decision-making, but only 2% operate completely autonomously. This highlights the need for careful oversight when using AI in financial decision-making.
The Future of AI in Finance
AI is set to become even more important in the financial world. AI models will continue to improve financial forecasting and regulatory compliance, making processes smoother and more reliable.
As AI technology advances, it will play an even bigger role in finance, transforming the way institutions operate.
For financial firms looking to stay competitive, adopting AI is no longer an option—it’s a necessity. Using AI technologies, businesses can work more efficiently, lower risks, and find new opportunities in the changing financial sector.
FAQs
How is AI used to improve financial services and products?
AI enhances financial services by automating risk assessment, fraud detection, and customer support. It enables personalised banking, predictive analytics for investments, and faster loan approvals. AI-driven chatbots improve customer interactions, while machine learning optimises finance operations.
How will AI change finance and banking?
AI will continue to automate routine tasks, reduce errors, and provide more accurate financial insights.
AI is becoming more important in decision-making. However, only 2% of AI systems work completely on their own. So human oversight is still very important. There will be changes to job roles and responsibilities.
Why is AI used in the finance industry
As fintech companies grow alongside traditional banks, AI helps them stay competitive in the market. AI is revolutionising the finance sector by enhancing efficiency, minimising risk, and elevating customer experiences.
We can can help you leverage AI’s potential while maintaining compliance, security, and adhering to ethical AI standards. Whether you’re looking to implement AI-driven automation, enhance risk management, or improve customer experiences, our solutions are tailored to meet your needs.
Get in touch with us today to explore how AI can transform your financial operations!
