AI in Finance: Use Cases, Strategies, and Best Practices

Finance businesses are perfectly situated to take full advantage of advances in AI. New technologies—such as decision and strategy automation—aid finance professionals to achieve complex work faster and to a higher degree of accuracy. This article explains why AI is used in finance and how you can take advantage with the help of dedicated AI consulting from Winder.AI.

Transform Your Business with AI

Within business, Artificial Intelligence (AI) is applied to the automation of decisions and strategies through the medium of data, like text in natural language or signals generated by natural phenomena. Anthropomorphically, these appear to the consumer as faculties like learning, reasoning, problem-solving, perception and language comprehension.

By integrating AI into your business operations, you can automate routine tasks, enhance decision-making processes, and provide predictive insights that can lead to increased efficiency and innovation. Embracing AI technology can help your business stay competitive in the rapidly evolving digital landscape.

Uses of AI In Finance

Finance has long used AI as part of a broader approach. A report from The Bank of England and Financial Conduct Authority suggests that use of AI continues to rise—with 75% of firms surveyed already using AI and a further 10% planning to use AI over the next three years.

As we’ve noted elsewhere, “Many forms of algorithmic training in finance,” for example, “attempt to discover, and then exploit, statistical inefficiencies in financial markets.” Models that achieve positive ‘alpha’ are kept secret for obvious reasons, but there are many use cases tthat are well advertised. AI is commonly applied to fraud detection, cybersecurity, clustering and profiling of both clients and transactions, as well as forecasting and business modelling. AI is also increasingly used for customer support (including chatbots).

The Bank of England reports that the use of large language models (a.k.a. foundation models) is rapidly gaining adoption. More than a third of financial businesses report using them. The research department is one of the largest users of LLMs within financial institutions. Of course the financial services industry also applies AI in places that are common across multiple industries, such as human resources, risk and compliance, IT operations and legal.

Use cases are fundamentally driven by business needs for scale and innovation, but success is driven by data. When you work with us, we endeavour to not only leverage the best data for the task, but also consult holistically to suggest a wide variety of future opportunities. Innovation in the long term requires iteration, and we hope you will continue to work with us for years to come.

Here are some examples of the wide variety of well-established use cases that Winder.AI engineers can deliver for you:

Benefits of AI In Finance

AI is, in every sense, revolutionising business. In nearly every industry, across all business functions, people are using AI to improve efficiency and automate high-intelligence tasks at scale. No more so than in finance, where AI primarily enhances efficiency through the automation of decisions and strategies.

AI-powered solutions are capable of processing vast amounts of financial data, with much greater speed and repeatability than manual processing. This analysis can progress tasks like predictive insights using customer information, forecasting of market trends, and strategic decision- making to manage risks.

The investment in AI is not limited to specific use cases. The incorporation of AI drives innovation in the wider organisation thanks to the reinvestment of newly-found time, and the collaborative experience of working with celebrated experts in the field. Successful innovation leads to market growth and long-term business viability. This is remarkable, since very few technology investments lead to both top-line market growth and bottom-line efficiency savings.

The Greatest Challenge for Finance and AI: Understanding the Need for Our Expertise

Whilst AI can offer unprecedented opportunities, integrating it into financial organisations is a great challenge. AI is rapidly evolving and optimal value extraction is technically complicated. Integration not only requires advanced technical knowledge but also a deep understanding of the statistical and operational principles. Since 2013, when Winder.AI began consulting in finance companies, AI has evolved significantly from analytic beginnings (data mining) to today’s applied creativity (GenAI).

Without expert guidance and applied AI technologists, organisations may struggle to achieve their objectives. The complex landscape of risk and regulatory compliance adds to the ethical burden experienced by finance businesses. Our expertise lies in bridging this gap to ensure that AI is implemented into your financial operations effectively, efficiently and responsibly.

Case Studies of AI in Finance by Winder.AI

Winder.AI has delivered a wide variety of AI projects for more than a decade. Here are a few case studies that we’re proud of; you can see more in our portfolio.

Reinforcement Learning In Finance

Our financial client is based in the UK. They specialise in providing services to the finance …

MLOps in Finance

Our client is a UK-based financial services company specialising in offering loans for car finance. …

Guide to Success: AI in Finance

Implementing AI in finance offers a powerful opportunity to enhance operations, improve compliance and deliver measurable ROI. However, to ensure success, business leaders need to address critical elements that drive outcomes and mitigate risks.

Key Success Factors

Successful AI projects require three essential components:

1. Clean, Reliable Data

Four of the top five risks to finance companies relate to the use of data. Data privacy and security is the top concern, alongside data quality and representativeness. The need for security is greater in finance than in other industries because of the sensitivity of the data. Data quality and representativeness is a key requirement in every AI application. Unified, secure and accurate data is therefore the foundation of AI success.

2. Clear, Measurable Objectives

A third of all use cases in finance are internal to businesses but, surprisingly, not specific to finance. This makes it much easier to iterate on creating ‘good’ goals that are clear, measurable and valuable. You should therefore define precise objectives tied to business outcomes, such as ‘reduce fraud by 25%’ or ‘increase loan approval efficiency by 20%’.

3. Expert Guidance and Cross-Functional Teams

After good data management and well-defined problems, the final key challenge is access to talent—represented as the largest non-regulatory constraint in finance. Because more than a third of all finance use cases are general, cross-industry experience is beneficial. But there remains a significant portion of use cases, approximately another third, which are finance specific and therefore benefit from domain experts in finance being part of the AI team.

It is therefore crucial to engage professionals who bridge AI expertise with a deep understanding of financial regulations, to ensure alignment with both technical and business needs.

Investment Planning

To achieve success, allocate resources strategically. For a mid-sized financial AI project, anticipate the following costs:

• Team: A team of two to three dedicated engineers (£100-200k annually each), corresponding to roles that match the work. You’ll also need a domain expert and someone to act as the product owner. Depending on how many projects you’re running, you might also consider dedicated project management staff.
• Technology: Initial data infrastructure investment of £50–100k annually, plus £25–50k for tools and training annually.
• Timeline: ROI realisation typically occurs within 6 to 12 months, depending on project scope and readiness.

You can start small with a pilot project to validate the ROI, then expand based on demonstrated results. This helps to minimise wasted spend.

Project Lifecycle

A successful financial AI project typically follows these key phases:

1. POC & Discovery (4 to 6 weeks):

   • Audit data sources and assess quality. This is the first step in any AI project and helps to establish the scope of the project.
   • Define compliance boundaries and project objectives. A clear understanding of the problem is crucial to the success of the project.
   • A small, iterative cycle of development and testing, with a focus on de-risking future development.

2. Core Development (3 to 4 months):

   • Build and test AI models using historical data. This is the core development phase, where models are built and tested. It is typically the most time-consuming phase but can be controlled through iterative development.
   • Collaborate closely with business stakeholders to refine outcomes. Avoid the temptation to over-engineer the solution at this stage. Instead focus on delivering value quickly.

3. Deployment & Refinement (1 to 2 months):

   • Roll out models gradually and focus on low-risk use cases initially to gain confidence. Buy in from stakeholders and leverage this to roll out to other areas of the business.
   • Incorporate feedback to optimise performance. Concentrate on problem areas that are most impacting the value. It is more often the user experience, not the model performance, that creates a bottleneck and requires a solution.

4. Maintenance (ongoing):

   • Monitor performance and update models as regulations, data or business needs evolve.
   • Invest in model logging and monitoring to ensure the solution is performing as expected.
   • Automate insights by integrating outputs with analytics and reporting tools.

Final Thoughts

AI in finance holds immense potential to transform operations, reduce costs and drive innovation. However, success hinges on clear objectives, aligned teams and thoughtful execution. By addressing common pitfalls and focusing on business outcomes, you can turn AI from a buzzword into a strategic advantage.

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