Why You Should Use AI in the Financial Industry and Focus on Value-Added Activities
The interest in Artificial Intelligence (AI) is constantly growing, especially in industries characterized by repetitive processes and manual tasks.
One benefit of the effective application of AI is that humans, freed from these repetitive tasks, are able to focus on higher-value activities. This is clear to the 41% of decision makers who are using cognitive and AI tools in their business, according to a recnet a Forrester survey, “Enterprises Adopt Cognitive and AI Tools,” as shown in Figure 1. In fact, many decision makers consider the technological progression to AI a major priority and already understand the potential of AI for their business.
Automation is an opportunity for financial industry, where AI can reduce the efforts of finance professionals in traditional activities such as transaction processing, auditing and compliance.
In fact, given the high volume and the nature of data in the financial industry, few industries are better suited for adopting artificial intelligence and keeping up with digital transformation. AI has come to play a relevant role in the industry where it is used in a wide range of applications across the financial system including forloan approval, insurance contracts, process automation, fraud detection, client interaction and claim management.
There are several potential benefits for the adoption of AI in the financial industry. They are:
- More efficient processing of information may contribute to a more efficient financial system. The application of AI by regulators and supervisors can help improve regulatory compliance and increase supervisory effectiveness.
- Easier access to data stored online has increased the security risk for data. Modern fraud detection goes beyond traditional financial fraud detection systems that depended on a complex and robust set of rules. This is where AI in the financial industry comes in play. Using machine learning, systems can detect suspicious activities or anomalies and flag them for security teams. The challenge for these systems is to avoid false-positives, where flagged “risks” are not actual risks. In this case, AI technologies are already being used in the financial industry to enhanced fraud detection and risk management capabilities (figure 3).
- Underwriting is an area where AI can make a difference because the algorithms can be trained on millions of examples of consumer data (age, job, status, habits, etc.) and financial results (credit and payment history, insurance status, etc.). The underlying information can be analyzed with AI to detect trends that might influence financial activities (such as for insurance or loans) in the future. These results are critical for all companies, especially those in the financial industry.
- Chatbots and conversational interfaces are considered the most promising AI consumer application in recent years. These virtual assistants use language processing tools to interact with customers and provide a high level of service, suggesting particular products or services tailored to their needs. Banks and financial institutions that offer chatbots for customer service have more possibilities to achieve better customer satisfaction because these applications may be perceived by customers as more trustworthy, objective, and reliable than human advisors. However, this application goes beyond the use of AI in finance, and is likely to manifest itself as specialized chatbots in a variety of fields and industries.
AI offers companies the ability to process vast quantities of data at a lower cost, to accurately process information with fewer errors, better outputs and improved speed. AI is an opportunity that cannot be ignored.
Any technology that can reduce manual work and human errors in financial industry will make more resources available for strategic tasks, whichn will always require human involvement.
Learn more about Expert System’s solutions for the financial industry.
Join our webinar “How Artificial Intelligence Is Transforming Insurance” to explore the promises and challenges of using AI across the insurance value chain, with a particular focus on underwriting. Click here for more information.