Artificial intelligence (AI) is poised to play a critical role in finance. From stock trading and portfolio management to financial forecasting, personal banking, and customer service, AI will help simplify what humans have been doing for decades: analysing complex data sets for actionable insights. Although the application of AI in finance is just beginning, a few noteworthy advancements are already taking place.
This blog discusses the five fundamental ways AI affects the financial services industry and how fintech companies should deal with this emerging technology.
Artificial Intelligence in Finance
When most people think of AI, the first thing that comes to mind is a computer simulation of a human brain. While true AI is arguably still years away, today's technology already integrates with human intelligence, allowing users to harness the power of computer processing. The applications are limitless, as are the data sources that artificial intelligence can move through.
In the financial services sector, artificial intelligence helps analyze various data sets, from bank transactions and customer profiles to social media sentiment – all in real time. This is key because AI quickly evaluates and makes decisions in ways that humans simply can't.
Gartner suggests that using AI in customer service alone will save banks 30 percent in salaries, with an overall savings potential of up to $8 billion. At the same time, customer satisfaction can be expected to increase by 25 percent since AI allows for faster response times.
Let's look at the main drivers shaping the future of AI in fintech.
The Main Drivers of AI in Finance
While many are familiar with the term "artificial intelligence," they may not know all the factors shaping this emerging technology's future. The use of AI in financial services is growing for several compelling reasons, as follows.
1. Rapid Growth in Big Data
In the recent past, big data has emerged as a powerful force driving business value. Big data refers to extensive, complex data collections that are too big and complex for traditional software tools and techniques to process.
To put this definition in context, IDC estimated that the world produced and consumed over 93 zettabytes (ZB) in 2022.
This volume will reach 149 ZB by 2025.This technology is used in finance to power services such as intelligent credit scoring, digital payments, personal banking, and customized financial advice. As a result, big data has become an essential component of AI-powered systems. The algorithms that evaluate big data are designed to collect, process, and analyze information from multiple sources within the organization.
2. The Rise of Chatbots
Financial institutions are becoming more innovative in their approach to customer service, which often comes in the form of intelligent chatbots and digital assistants for websites, mobile apps, and social media platforms.
Two key factors drive the rapid growth of chatbots:
- Consumers are increasingly looking for more convenient ways to interact with fintech service providers. They want to be able to access information and services without having to interact with a human representative.
- Chatbots help manage customer inquiries and high-value transactions. In fact, chatbots have been proven to impact customer satisfaction directly.
The rise of chatbots is a response to this growing appetite for convenient customer service.
3. Personalization
As customers become increasingly familiar with the products and services of fintech companies, they no longer want to be treated like a commodity. Instead, customers today seek personalized services, which is driving fintech firms to focus on improving customer satisfaction.
This factor comes together with data analytics, which gives companies insights into how to improve their products and services. And when data analytics combines with AI, fintech companies can identify customer preferences and deliver a more personalized experience.
For instance, many fintech companies use AI for customer segmentation, which lets them create tailored offers for each segment. This personalization enables providers like Fiserv, Intuit, and Stripe to increase retention rates. Customers are more likely to stick around if they feel they're being treated as individuals.
4. Improved Cybersecurity
Financial services companies are more susceptible to cyber attacks, and the growing threat of financial fraud has forced them to take cybersecurity very seriously.
Financial institutions face different cyber threats, including data breaches, viruses, and hacking. These attacks can cost them millions of dollars and put the integrity of customer data at risk. Therefore, combating cyber threats and protecting customer data is an essential priority for fintech companies.
The way to achieve this will be through AI-based solutions capable of anticipating threats and alerting businesses when they occur.
For example, machine learning could help financial institutions detect suspicious emails and phone calls before they reach their target customers. In addition, this technology will help protect customer data and avoid major cybersecurity scams, which is crucial given the industry's growing threats.
5. Decreasing Costs
Perhaps, the biggest challenge for fintech companies moving forward is finding the right balance between higher-cost, high-end services, and low-cost services. Customers demand more from their fintech providers and are willing to pay. This trend forces companies to find new ways to deliver more value at a lower cost.
AI technology offers a helpful approach to this problem. For example, financial institutions like Barclays and Fidelity adopt AI solutions to support their high-net-worth clients effectively. At the same time, they provide due diligence and compliance advice to many wealth management customers at a fraction of the cost.
So, the goal is to provide more sophisticated, high-value fintech services at a fraction of the cost. According to an Accenture report, AI will help U.S. financial services companies reduce costs by 45%. This improvement will come from reducing spending on infrastructure and personnel, as well as having a direct impact on revenue.
How to Turn AI into ROI
The future of finance looks promising, thanks to AI technology. AI will help financial institutions improve customer engagement and reduce costs, leading to higher profitability.
However, many businesses find investing in AI and machine learning difficult. For these organizations, it's important to remember that AI isn't a solution to every business problem.
Remember: there is no single recipe for success with AI. The secret to making it work is experimentation.
First, you'll have to pick the correct data and define your goals. Then, you'll have to create and test your AI models. Finally, you'll need to consider the right AI tools. For example, you use production-ready machine learning APIs from a wide range of AI development vendors such as Enova, Zest AI, and Simudyne. Or, you hire a dedicated team of AI developers to design, code, and implement a model tailored to your company's business needs.
The process is challenging, but it's worth the effort. Your organization can adopt a broad range of successful AI solutions covering everything from threat detection to fraud prevention and multi-factor authentication.
Increasing ROI through AI is all about experimentation and trial and error. And as you continue to work on ways to turn AI into ROI for your company, it's essential to be aware of some of the obstacles likely to stand in your way.
Wrapping Up
Artificial intelligence could be one of the most disruptive forces in the financial services industry. Right now, many companies are getting ready to reap the benefits of this technology.
Fintech companies will increasingly incorporate AI into their business models. This trend is driven by many factors, including customers' greater appetite for self-service, personalization, innovation, and cybersecurity. As a result, AI will help these organizations improve customer engagement, reduce costs, and increase profitability.
Fintech companies will have to experiment with and test various AI solutions with their business models in mind. There's no "one-size-fits-all" approach to AI, but that's not a problem. How to turn AI into ROI for your company is a question of experimentation, trial, and error.
About Author:
Dmytro is a marketing professional at Intersog, a Chicago-based IT company recognized for its tailored approach to client software engineering and IT staffing.
