Artificial intelligence (AI) is revolutionizing the finance industry, from stock trading and portfolio management to financial forecasting, personal banking, and customer service. By analyzing large volumes of data in real-time, AI simplifies the process of extracting actionable insights from complex datasets, a task that humans have been doing for decades. While the application of AI in finance is still in its early stages, significant advancements have already been made.
Artificial Intelligence in Finance
When many people think of AI, they envision a computer simulation of a human brain. While true AI may still be years away, today’s technology already incorporates human intelligence, allowing users to leverage the power of computer processing. The potential applications of AI are limitless, as it can process data from diverse sources.
In the financial services sector, AI is used to analyze various types of data, such as bank transactions, customer profiles, and social media sentiment, in real-time. This capability is critical as AI can quickly evaluate data and make decisions in ways that are not possible for humans.
According to Gartner, the use of AI in customer service alone can result in a 30% reduction in salaries for banks, with a potential overall savings of up to $8 billion. At the same time, customer satisfaction is expected to increase by 25% due to faster response times enabled by AI.
The Main Drivers of AI in Finance
While many are familiar with the term “artificial intelligence,” they may not be aware of all the factors that are shaping the future of this emerging technology. The use of AI in financial services is being driven by several compelling reasons, as outlined below.
- Rapid Growth in Big Data: Big data has emerged as a powerful force driving business value in recent years. It refers to large and complex collections of data that traditional software tools and techniques are unable to process.To put this into perspective, it is estimated that the world produced and consumed over 93 zettabytes (ZB) of data in 2022, and this volume is projected to reach 149 ZB by 2025. In the finance industry, big data is powering services such as intelligent credit scoring, digital payments, personal banking, and customized financial advice. Consequently, big data has become an essential component of AI-powered systems, with algorithms designed to collect, process, and analyze information from multiple sources within organizations.
- The Rise of Chatbots: Financial institutions are adopting more innovative approaches to customer service, including the use of intelligent chatbots and digital assistants for websites, mobile apps, and social media platforms.The rapid growth of chatbots is driven by two key factors. First, consumers are increasingly seeking more convenient ways to interact with fintech service providers, without the need for human representatives. Second, chatbots are effective in managing customer inquiries and high-value transactions, directly impacting customer satisfaction.The rise of chatbots is a response to the growing demand for convenient customer service in the finance industry.
- Personalization: As customers become more familiar with the products and services offered by fintech companies, they no longer want to be treated as commodities. Instead, customers now expect personalized services, driving fintech firms to focus on improving customer satisfaction.This trend is supported by data analytics, which provides companies with insights on how to enhance their products and services. When combined with AI, data analytics enables fintech companies to identify customer preferences and deliver a more personalized experience.
- Enhanced Cybersecurity: The susceptibility of financial services companies to cyber attacks has heightened the importance of cybersecurity in their operations. Financial institutions face a range of cyber threats, including data breaches, viruses, and hacking, which can result in significant financial losses and compromise the integrity of customer data. As a result, safeguarding against cyber threats and protecting customer data has become a top priority for fintech companies. The solution lies in AI-based technologies that can proactively anticipate threats and provide real-time alerts to businesses. For instance, machine learning can aid financial institutions in detecting suspicious emails and phone calls before they reach their intended targets, thereby safeguarding customer data and preventing major cybersecurity breaches. This becomes even more crucial in light of the increasing threats faced by the industry.
- Cost Reduction: Striking the right balance between high-cost, premium services and low-cost offerings is a key challenge for fintech companies as customer demands evolve. Customers now expect more value from their fintech providers and are willing to pay for it, necessitating companies to find innovative ways to deliver superior services at reduced costs.AI technology offers a viable solution to this challenge. For example, financial institutions such as Barclays and Fidelity are leveraging AI solutions to effectively support their high-net-worth clients while also providing due diligence and compliance advice to wealth management customers at a fraction of the cost. The goal is to deliver sophisticated, high-value fintech services at a reduced cost, and according to an Accenture report, AI has the potential to reduce costs for U.S. financial services companies by up to 45%. This can be achieved by reducing spending on infrastructure and personnel, and directly impacting revenue.
Maximizing ROI with AI
The future of finance appears promising with the integration of AI technology. AI can enable financial institutions to enhance customer engagement, reduce costs, and ultimately drive higher profitability.
However, investing in AI and machine learning can be challenging for many businesses. It is important to note that AI is not a one-size-fits-all solution to all business problems.
It is crucial to experiment with the right data and define clear goals, create and test AI models, and carefully select appropriate AI tools.
Although the process may be challenging, the potential benefits are worth the effort. Organizations can adopt a wide range of successful AI solutions, including threat detection, fraud prevention, and multi-factor authentication, to enhance their ROI through AI. Experimentation and trial and error are key to turning AI into ROI for your company, and it is important to be aware of potential obstacles along the way.
Conclusions
Artificial intelligence has the potential to be a highly disruptive force in the financial services industry, and many companies are gearing up to harness its benefits. Fintech companies are increasingly integrating AI into their business models driven by factors such as customer demand for self-service, personalization, innovation, and enhanced cybersecurity. As a result, AI will play a crucial role in improving customer engagement, reducing costs, and driving profitability for these organizations. Experimentation, testing, and customization are key to unlocking the full potential of AI and achieving optimal ROI for your company.