How Artificial Intelligence is Changing Fintech: Top Trends in 2024

AI integration in financial companies can improve operational efficiency, customer experiences, and fraud detection. Here’s how.

AI’s role in fintech is increasingly perceived as that of a copilot for handling administrative tasks. For instance, AI can help generate quotes, draft contracts, review and analyze customer contracts for specific terms, and even recommend additional products that customers might require based on their purchase history. This not only saves time but also personalizes the customer experience.

Artificial intelligence can optimize smart case management workflows, streamline unstructured communications, and handle bank-to-bank financial crime discussions. In fact, it can increase company efficiency by as much as 25%.

With AI, the financial sector may use advanced data analytics, particularly in predictive modeling. Financial institutions can tailor their products and services to individual customers by accurately microtargeting them. Additionally, fintech companies are more likely to be innovative and creative when AI is used for rapid prototyping and A/B testing.

Even when dealing with a high volume of transactions, robotic process automation enabled by AI allows for more speed and accuracy. Thanks to this technology, transaction processing is now in conformity with the most recent rules and regulations. Quick settlements made possible by real-time processing make financial operations safe and efficient.

Artificial Intelligence plays a critical role in enhancing security measures. It does not just detect fraud but also proactively searches for potential risks and alerts individuals of dangers. With the help of machine learning algorithms, AI systems can continuously adapt and evolve, making it possible to identify new fraudulent strategies.

A growing number of companies are using AI and ML to streamline their operations and provide better customer service. For the financial market, however, the trend towards AI brings both possibilities and threats.

That’s why the implementation of new AI-driven features requires thorough preparation.

• There are a lot of problems with integrating AI with current solutions, such as compatibility issues with legacy systems and centralization of data silos.
• For AI to be fair and transparent, it needs to be able to make decisions without bias and explain its reasoning clearly.
• To successfully traverse regulatory changes, AI applications must be compliant with data protection regulations and financial rules.
• To achieve a return on investment (ROI), matching AI projects with strategic goals and building customer trust in AI systems is essential.
• In addition, keeping consumer data private, secure, and reliable is critical to gaining their approval.
• Building AI solutions and integrating AI-driven features is challenging and requires an in-depth knowledge of AI technologies and financial processes. These projects rely on the availability of a wide range of skills and resources.
• For artificial intelligence projects in the financial technology sector to progress, there is an urgent need to fill the gaps in talent and expertise in data science, AI development, and software engineering.

We often marvel at AI’s promises, from battling fraud to automating financial operations. However, implementing artificial intelligence in fintech has its share of problems.

Let’s look at some examples.

The case of Metromile

Metromile, a car insurance technology company, wanted to transform the industry by using AI to detect fraud and simplify insurance claims, especially for drivers who travel fewer miles. The company used machine learning to evaluate data from low-speed staged accidents to speed up the claim processing systems.

Their new AI-powered insurance model used a pay-per-mile pricing structure to streamline claims processing, cut down on fraud, and attract more clients. Nevertheless, Metromile encountered challenges, leading to a substantial stock value drop. In this case, issues with applying AI didn’t arise from technology but from market and operational factors.

SEC filings interpretation challenges

One way to speed up and improve the efficiency of financial analysis is to utilize AI to decipher Securities and Exchange Commission (SEC) filings. The complicated financial information and terminology in these filings make it difficult for AI to interpret them accurately.

According to a recent study, even the most advanced AI models face challenges when answering queries obtained from SEC filings. OpenAI’s GPT-4-Turbo, currently considered the best-performing model, could correctly answer only 79% of questions in a particular test setup. And it still experienced some limitations, such as generating incorrect data or refusing to answer certain questions.

If AI models cannot provide consistent and accurate financial information, can we effectively apply them across the fintech industry?

The answer is Yes. Despite some pitfalls, AI in fintech plays a crucial role across various applications, directly impacting operational efficiency and customer interaction. And as such, it is widely used across the industry:

• Fraud detection: AI’s ability to analyze transactions for irregularities is vital for preventing fraud. With AI in the fintech market set to grow significantly, reaching $39.44 billion by 2028, its role in fraud prevention is more important than ever, driven by a substantial focus on safeguarding assets and reducing losses.
• Virtual assistants & chatbots: They’re becoming the front line of customer service, offering instant responses to queries. This AI application is an exciting use case, reshaping customer interactions by providing 24/7 service and significantly enhancing users’ banking experience.
• Customer behavioral analytics: By diving deep into customer data, AI helps financial institutions understand patterns, preferences, and behaviors. This insight allows for more tailored products and services, improving customer satisfaction and loyalty.
• AI-based financial advisors: Also known as robo-advisors, these tools use AI to provide personalized investment advice. They’re making financial planning more accessible and affordable, democratizing investment advice beyond high-net-worth individuals.
• Credit risk scoring: AI algorithms offer a more nuanced credit risk assessment by analyzing traditional and alternative data sources. This can lead to more accurate risk assessments and potentially open up credit to underserved markets.
• Personalized customer service: AI enables a more customized interaction for banking clients, from personalized banking advice to proactive fraud alerts. This not only improves customer experience but also builds trust and engagement.
• Tailored marketing campaigns: A survey by Epsilon indicated that 80% of consumers are more likely to make a purchase when brands offer personalized experiences. That’s why financial institutions are using AI to craft marketing messages that resonate on a personal level with their audience. By analyzing customer data, AI can help tailor offers and promotions to match individual needs and preferences, increasing conversion rates.

Here are some case studies showing how different companies in the sector implement AI. Each example underscores the technology’s potential to boost operational capabilities and create new value propositions in the competitive landscape.

Zest AI uses powerful machine learning to deliver accurate insights on borrowers and automate more of the lending process. Their AI-driven lending software helps lenders approve more applicants without increasing risk by using hundreds to thousands of variables for a more comprehensive risk assessment, compared to the 20 or so data features used in traditional credit scoring models.

Lemonade has changed the insurance industry with AI-driven methods for managing claims and underwriting policies. The company uses chatbots to interact with customers and machine learning to speed up the claims process, significantly reducing costs and improving customer service.

There are many cases of AI being used for anti-money laundering and fraud detection services.

Citi has invested in Quantifind, an AI-powered financial crime risk management technology provider. The Quantifind platform is helping Citi improve the accuracy and efficiency of its AML monitoring and compliance efforts.

Valley Bank used an AI Platform to enhance its anti-money laundering efforts, successfully reducing false positive alerts by 22% and improving case escalation by three percentage points. The platform allowed for the automation of predictive modeling, significantly reducing the manual workload and enabling the creation or retraining of models within a day, a process that previously took weeks. 

And then, we have a fascinating case of robo-advisers, the market of which is expected to grow to US$2,334.00bn by 2028. NerdWallet’s list of the best robo-advisors as of April 2024 includes prominent names like Betterment, Wealthfront, Vanguard Digital Advisor, Fidelity Go, SoFi Automated Investing, and Stash. These companies are recognized for their contributions to the fintech industry through their advanced use of AI and algorithm-based financial advising and portfolio management services.

Scalo steps in with smart, practical solutions to empower fintech companies to navigate the AI landscape. Here’s how we make a difference in the finance industry:

Ideation Workshops: These workshops are brainstorming sessions that help fintech firms pinpoint practical AI applications and plan their implementation strategies.

Expert Talent Access: Scalo connects fintech companies with top-notch AI and data science professionals, filling talent gaps.

Managed Data & AI Teams: Scalo offers specialized teams to ensure fintech firms have the resources to move their AI projects from concept to execution without a hitch.

Growth & Efficiency: Partnering with Scalo means fintech firms can speed up growth and boost efficiency by integrating AI smoothly into their operations.

Insightful Industry Expertise: Scalo brings deep insights into applying AI in finance, helping firms navigate the tech landscape and regulatory requirements.

Agile Innovation: With our support, fintech companies can quickly adapt to new technologies and market changes, fostering an environment always ready for innovation.

End-to-End Solutions Development: Scalo covers all bases, from ideation to deployment, ensuring fintech firms make the most of AI technologies.

Team Augmentation: Our flexible team solutions allow firms to expand their capabilities easily, tapping into a global talent pool.

Post-Development Support: Beyond deployment, you get continued support to ensure AI solutions stay updated and optimized, meeting both current and future needs.

In essence, Scalo equips fintech firms to use AI effectively. We aim to bridge the gap between technological potential and real-world application to drive innovation and ensure business growth.

Contact us for more information.