AI in Financial Services 2024: Transforming Client Experience and Marketing

man discovering the possibilites of ai in financial services

AI in Financial Services: Transforming Client Experience and Marketing

Artificial intelligence (AI) isn’t just a buzzword. It’s a pivotal field of computer science dedicated to crafting clever “machines” that mimic human behaviour.

Picture a robot that not only chats about the weather but also predicts the stock market’s next big dip—all while brewing a cuppa!

While we recommend you leave the brewing to your office assistant, for now, the above attempt at humour showcases how versatile AI in financial services can be.

There’s no one way to use AI, and you don’t have to go all in 100% AI to benefit. You can start right now by exploring some innovative ways to use AI in banking, insurance, wealth management, and more.

The simple truth is this!

These self-taught whizzes are capable of organising vast amounts of data, analysing it, and making forecasts that shape futures. That’s beyond what a tower full of humans can do right now, and you can use that efficiency to help the humans in your organisation work smarter, faster, and better, all while enhancing client services.

In the UK alone, the AI market is expected to top £5B by the end of this year with a CAGR of 28.30% over the next 5 years. In the bustling corridors of the Banking, Financial Services, and Insurance (BFSI) Industry, AI is not just a guest anymore—it’s part of the family, revolutionising the way services are rolled out and shaking up the traditional teapot.

In this article, we explore:

  • Practical use cases for AI in finance
  • The Human Impact of AI
  • Legal Considerations of AI Technology
  • Step-by-Step how to start using Financial AI

Frequently Asked Questions About AI in Financial Services

Everything you need to know about AI in Financial Services.

Statista Research conducted a massive global survey in the financial sector. Here’s what they found.

43% of organisations name efficiency as the biggest benefit, with a competitive advantage not far behind at 42%. Accuracy, reduced costs, and new business opportunities all made the list.

However, you may be surprised to discover that 27% said that the greatest benefit they saw was in their ability to deliver client services and support more effectively.

Improved accuracy, efficiency, and cost savings are helping financial organisations raise the bar on client experience. AI is revealing new opportunities to grow in an evolving marketplace.

How does the use of AI in financial services drastically improve operational efficiencies? Imagine processing loans in minutes, not days! AI in financial services speeds up data analysis, risk assessment, and decision-making processes, transforming traditional wait times in the blink of an eye.

Today, AI is used in financial services primarily to automate tedious processes. It improves the data processing efficiency and accuracy by freeing up humans for more complex, creative, and mentally-engaging tasks.

Imagine a world where your financial services technology knows your needs before you do – that’s not just science fiction. It’s happening now.

AI can automate many financial services across a broad range of tasks, from customer service to risk management. But at the same time it learns about your business, clients, competitors and more. It can spot business needs you didn’t know you had, provide viable solutions, and even implement them for you.

That’s the power we’re talking about here.

How AI in Financial Services is Making An Impact

But I want to put this into perspective. How does AI help you in the real world? Here are some key areas where AI is making a significant impact.

Customer Service and Personalisation

Chatbots and Virtual Assistants are AI-driven tools that can handle and personalise a vast number of your clients and future clients’ queries in real time. From account balance inquiries to transaction support to investigating suspicious activity, conversational AI for banking can often take care of a client without human intervention—or the need for rest breaks.

Now, I know this may sound a bit impersonal on the surface. However, in practice, it’s highly personalised banking and finance at its core.

67% of consumers prefer self-service options. 73% say providing good online service is the most important improvement that a company can make.

Every chat is different and guided by the clients themselves. People like chatbots when they’re done right, and that requires AI.

People who need support don’t have to sift through pages of irrelevant information. AI helps them find precisely what they’re looking for fast.

But it’s not just a search engine.

AI algorithms can further personalise client experience by analysing customer data to provide tailored banking advice, product recommendations, and even financial planning services. These are all specifically designed around the client’s needs and goals.

AI marketing can support human financial advisors by sorting through client information that would take them months to review.

When you use AI to deliver an elevated client experience, that becomes a reason for more clients to hire your firm and use your services. Through client-centred AI-powered interactive website experiences, it becomes easier to showcase your value on your website 24/7.

Fraud Detection and Risk Management

AI excels in identifying patterns and anomalies in transactions that may indicate fraudulent activity significantly faster than traditional methods. It can then stop transactions or alert teams for further analysis.

Integration of AI into the blockchain can enhance security for crypto transactions, allowing for transparency while maintaining privacy.

By evaluating a wide range of variables, AI models can predict the likelihood of a borrower defaulting, thus aiding in more accurate credit scoring and risk assessment. This allows AI to deliver decisions that once took days in a matter of minutes.

Operational Efficiency

Robotic Process Automation (RPA) powered by AI can perform repetitive tasks such as:

  • Data entry
  • Data cross-population in multiple forms
  • Compliance checks
  • Report generation
  • Document analysis
  • Summarising information and highlighting what’s important


AI can also help you manage hand-offs between teams. Take, for example, AI-powered lead-scoring software. This essential marketing-sales alignment tool can smooth the transition of a marketing lead to a sales-qualified lead by analysing that lead’s behaviour. It can compare anonymised data from other leads who have become paying clients and perfectly time the hand-off to sales.

AI can even assess its own performance to teach itself how to continually improve these handoffs.

Compliance and Audits

AI can keep track of changing regulations and help financial institutions remain compliant by monitoring transactions and flagging outliers that may require closer inspection.

Automating the generation of key reports needed for regulatory purposes not only saves time but also improves accuracy.

AI is not just a tool but a game-changer in the financial services sector, providing smarter, faster, and more personalised services while enhancing security and compliance.

Tracking, Reporting, and Optimisation

The machine learning side of artificial intelligence allows it to spot trends and adapt to them, often without direct human intervention.

Use AI to track ad performance, identify the most effective financial services videos and copy with each audience and automate who sees which ad. Not only do you see a measurable increase in PPC Ad click-through and the corresponding increase in revenues. This increased advertising effectiveness reduces your acquisition costs as more prospects are taking desired actions that lead to meeting business goals.

It’s all possible when you integrate AI into your advertising campaigns.

Impact on Humans of AI in Financial Services

It’s an important aspect to consider. Many imagine AI will replace humans. However, that’s not what history teaches us. Humans evolve with technology. Job roles and processes are redefined and often redesigned.

Yes, in an AI-integrated financial organisation more roles focus on managing AI systems and deriving strategic insights rather than performing routine tasks. However, the people who once performed those brain-numbing activities day in and out are also the most capable of using AI to make their jobs faster and more efficient.

On the client side, we can expect consumer personalisation expectations to rise as AI makes the personalisation of user experiences easier for financial companies. As this happens, your AI-integrated, more effective organisation can meet those expectations.

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What About AI in Financial Services Laws?

In the UK, the financial services sector is closely regulated to ensure safety, integrity, and fairness, and as AI technologies become more integrated, regulations have begun to adapt. This highlights the importance of working with an AI consultant who understands the complex regulatory environment in which you work and can simplify compliance. Here are some examples of how AI intersects with financial services laws in the UK

    1. GDPR (General Data Protection Regulation): While GDPR is a broad EU regulation, it has significant implications for AI in the UK. Your AI systems that process personal data must comply with GDPR’s principles. You must also incorporate data protection by design and by default.
    2. Data Ethics: The UK’s Centre for Data Ethics and Innovation provides guidelines and frameworks to ensure you are using AI ethically, especially concerning data usage, which is critical in financial services.
    3. Sectoral Considerations for the UK Information Commissioner’s Office: This outlines legal obligations for both your developers and those using the technology (your team).
    4. Bank of England’s AI Public-Private Forum (AIPPF): This initiative was launched to explore the use and impact of AI in financial services, including sharing best practices and potential policy responses.
    5. FCA (Financial Conduct Authority) Regulations: The FCA is increasingly interested in how you are using AI and machine learning in finance, particularly in consumer-facing functions. The FCA ensures that AI does not lead to unfair outcomes or systemic risks and that firms maintain transparency with consumers about how their data is used.
    6. Equality Act 2010: There have long been concerns about AI potentially leading to biased decisions, especially in credit scoring and risk assessments. These concerns have to be navigated within the bounds of the Equality Act, which protects individuals from discrimination.
    7. AML (Anti-Money Laundering) Regulations: AI tools are increasingly used to detect and prevent financial crimes such as money laundering. The UK’s AML guidelines require financial institutions to implement effective systems and controls, which can include AI technologies, to combat financial crime.

How to Integrate AI into Your Financial Organisation - Your 7 Step Plan

Step 1: Assess Your Needs and Goals

Remember: You don’t have to do this all at once or go all in 100%—unless you have an unlimited budget.

That would be one I haven’t heard in a while.

We begin by identifying the specific areas within your organisation where AI could add the most value. Very often, that will be in customer-facing AI applications like:

  • Interactive website tools
  • AI chatbots (they’re not all AI)
  • Content distribution automation
  • Advertising campaign optimisation
  • Personalisation

Once you pinpoint these areas, set clear, measurable goals for what you aim to achieve with AI, such as reducing operational costs, elevating customer satisfaction, or improving decision-making processes.

Step 2: Build or Enhance Your Data Infrastructure

AI’s effectiveness hinges on the quality and relevance of the data it learns from. Start by ensuring that you have access to high-quality data that is pertinent to the problems you are trying to solve.

Implement robust data management practices to maintain the accuracy and integrity of your data, which should include:

Routine Data Cleaning: Correcting or removing inaccurate, incomplete, duplicated, or irrelevant data from datasets, ensuring high-quality information for analysis and decision-making. Hint: AI can help you with this, too!

Normalisation: You’re organising data in a database to reduce redundancy and improve data integrity going forward. It involves structuring a database according to a set of norms to minimise duplication and simplify data relationships, making the data easier to manage, update, and query.

Secure Storage Procedures: Keep client and other business data safe and accessible to those authorised to view it.

Step 3: Choose the Right AI Technologies

Conduct thorough research to explore the various AI technologies and tools that best meet your specific needs. Whether it’s natural language processing (NLP) for enhancing chatbots or machine learning models for predictive analytics, understanding the landscape of available technologies is crucial.

You must also decide whether to develop AI solutions in-house or collaborate with external AI technology providers. When selecting an AI consultant to help you with this task, consider their experience in the financial sector and their compliance with applicable regulations.

Step 4: Ensure Compliance and Ethical AI Use

It’s critical to familiarise yourself with the legal and regulatory requirements we mentioned above. Your AI solutions must comply with these regulations. Moreover, commit to ethical guidelines for AI use to ensure that your AI systems are fair, transparent, and accountable.

This includes conducting audits on algorithms to check for biases and implementing methods to explain AI decisions.

This clarity is critical in the financial sector. Discriminatory decisions are not only ethically wrong. They could get you into legal trouble. Historical bias in decision-making could influence future decision-making if you’re not careful.

Step 5: Implement Pilot Projects

At JReece Digital, we’re firm believers in starting small and manageable, so you can starting generating the ROI you need to see to justify further investments in AI technology.

Test AI solutions in controlled environments. This approach allows you to monitor AI’s impact, make necessary adjustments, and assess the outcomes against your objectives. Successful pilots can then be scaled up and integrated more broadly across your organisation.

Step 6: Train Your Team

As AI technologies are implemented, it’s essential to train your staff not only on how to use these new tools but also on the underlying principles of AI. This knowledge empowers them to make better decisions and facilitates smoother integration of AI into existing workflows.

Step 7: Evaluate and Scale

After the pilot projects, evaluate their effectiveness based on the predefined metrics and goals. Identify any areas for improvement, and adjust your strategies accordingly. If the pilots are successful, plan a gradual rollout of AI technologies across other areas of your organisation, scaling the solutions to take full advantage of their capabilities.

The Future of AI in Finance

There is no perfect way to predict how AI will change finance in the next 5 to 10 years—or even this year. But one thing is clear. AI is here. It is changing how we do business. And it’s continuing to evolve. Are you evolving with it?

By following these steps, you can effectively integrate AI into your financial services operations, positioning your organisation at the forefront of innovation and efficiency.

You see the writing on the wall and the potential of AI. But you’re unsure how to get started. Which AI marketing automation solutions will propel your company into the future?

Let’s Begin Your Journey

Book a 30 minute free chat with me and let’s discuss your business goals and start evaluating where you can experience the greatest AI ROI.

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