In computer science, artificial intelligence (commonly shortened to AI) is a field that allows the development of computers that can carry out jobs usually done by people. Its main focus is towards increasing thinking and intelligence in machines. It increases the efficiency of tasks and lowers costs while trying to simulate human behavior. The development of AI systems falls into three areas – the first part involves building systems that think like people; the second part is to get the job done by those who look at the computation models; and finally, the third part involves building systems that inform and inspire but do not imitate.
In today’s technological era, banks are emphasizing on client needs and prioritizing their services accordingly. Their focus is on providing personalized financial services in FinTech (financial infrastructure that drives consumer enablement). AI in FinTech solutions has changed the way banks and insurance companies function. Since FinTech uses data and technology to address the challenges faced by the financial services industry, AI is essential to FinTech’s existence and usage. As customers conduct more and more payment transactions on mobile phones, FinTech companies are using modern technology to set up new business models.
Financial services require information exchange that is reliable, secure, and efficient. The digital world has the potential for new alternatives that can be cheaper and quicker than traditional models. AI in FinTech has completely transformed the traditional banking system. The five pillars of FinTech: social media, analytics, artificial intelligence, blockchain, and digitization, can help FinTech companies to take on not only existing financial challenges but also security and regulatory dilemmas associated with artificial intelligence.
The InsurTech industry deals in the collection, processing, and analysis of high volumes of data. Artificial intelligence helps the insurance industry process information more efficiently. AI can minimize errors in InsurTech by providing easy-to-use algorithms and can provide more accurate risk prediction results to improve the quality of services consumers receive.
How AI can be applied in banking
Artificial intelligence acts as an accelerator that allows financial institutions to innovate new solutions and provide products and services that are technologically progressive.
Analytics: AI-guided analytics examine vast quantities of data to find patterns, groupings, and correlations. This allows the financial industry to move from descriptive to predictive (near real-time) analysis.
Chatbots: A chatbot is a service based on technical algorithms. It interacts with customers in a human-like manner.
Robotic Process Automation (RPA): RPA relies on a number of techniques to repeat routine human activities automatically and accurately. RPA receives inputs (digitally or paper-based), examines them, applies a rule, and then sends the results towards the next step in the process.
Report Generation: Natural language generation (NLG) transforms data into expressive text that is easy to understand. Using large amounts of this structured data, reports, and summaries can be written and put into a prose narrative style that highlights their key points.
Applications of AI in Financial Services
Accurate Decision-making – Data-driven management decisions at lower costs help banking personnel get the right answers from machines instead of on humans. The system analyzes input and comes up with recommended results that can help leaders and their subordinates take better decisions.
Automated Customer Support – Customer-facing systems such as text chats, voice systems, and chatbots can deliver human-like customer service at low costs. These solutions are capable of acting like Personal Financial Assistants for customers, thus leading to institutions saving time and money.
Fraud Detection and Claim Management – AI tools monitor user behavioral patterns to identify unusual activity and warning signs of fraud. Then, analytics tools are used to analyze the data for confirmation. Machine Learning (ML) techniques can be applied by claims management teams in different stages of the claim handling mechanism. Using self-learning capabilities, artificial intelligence can quicken certain claims by processing huge amounts of data in a relatively short period of time. It can analyze and identify different data patterns in order to recognize fraudulent claims. This results in automation of handling mechanisms by reducing the overall processing time and handling costs while enhancing customer experience.
Automated Virtual Financial Assistants – Automated virtual financial assistants and planners assist users in making financial decisions. They provide assistance by monitoring events as well as trends in stock and bond prices while keeping track of the user’s financial goals and personal portfolio. Automated virtual financial assistants can help recommend whether to buy or sell bonds and stocks. These systems are called ‘Robo-Advisors’ and are offered by both established financial companies and FinTech startups.
Judging by the swift increase of automation in the financial industry, the application of AI tools holds significant potential benefits. Modern techniques such as artificial intelligence, machine learning, and bots in finance have the potential to reduce costs, expand skills, and improve customer experience in the financial sector. In the modern world of technology, financial institutions should always be competent. They can achieve this goal by regularly updating their technology and making sure new concepts are diagnosed, developed, and commercialized effectively. This requires financial companies to work closely with coders, developers, designers, and other technical professionals.