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The Impact of AI on the Finance Industry
Technology - February 5, 2021
A race towards digitization is bringing a revolution in the Financial and FinTech sectors. At the core of this digitization lies the availability of a vast array of data (such as Big Data), advancements in affordable computing technologies, and the advent of intelligent technologies such as Machine Learning and Artificial Intelligence.
AI has been around for nearly 70 years, its practicality and intelligence have increasing over time. Today, AI has become an integral part of the industrial landscape as well as the lives of common people. Examples of this can be seen in the voice assistants in smartphones, the use of AI robots in supply chain logistics, self-driving cars, movie recommendations on Netflix, and more. The evolution in AI capabilities is coupled with our own technological and ecological advancement as AI moulds itself to human intelligence to support us in our personal and industrial activities.
AI has excelled in certain industries and most times, its application is cross-industries as well. A McKinsey report predicted that AI-assisted organizations are better equipped to scale their operations and reap greater profit margins. Examples of critical industries where AI is adopted in the existing landscape are FinTech and Finance.
A snapshot of the immediate benefits of Artificial Intelligence in the Financial sector is listed below:
• Secures financial transactions
• Reduces operational costs
• Improves user experience
• Enhances productivity
• Automates several processes
AI and Machine learning algorithms are designed to identify patterns. They detect similarities among a multitude of sequences and events and extract useful information from the vast data sets. These patterns can help FinTech providers to identify new business opportunities and develop coherent strategies. In the passage below, we will explore different application areas of AI in Finance and discuss the advantages.
1. Credit decisions
AI facilitates a quicker and more accurate assessment of a potential borrower and helps financial institutions determine the creditworthiness of that borrower. The process is objective, less expensive and comprised of a variety of factors that help banks make more accurate decisions backed by data. This data also spans various domains and includes smartphone and other personal details that present a more comprehensive picture of the borrower's spending habits and patterns.
AI is transforming credit scores by introducing complex and sophisticated rules to help financial institutions distinguish between high default risk applicants and credit-worthy applicants lacking a detailed credit history.
2. Risk management
Risk management is another area that has been transformed with the application of AI. Today, financial institutions have a better array and quality of data at their disposal as Machine learning models process big data with great speed and accuracy. In addition, AI can comprehensively analyse the risk cases histories and quickly detect discrepancies. Overall, it is a powerful ally as it analyses real-time market and financial activities and churns out more accurate prediction forecasts.
3. Fraud prevention
Online fraud has been growing at a rapid pace and AI is very effective at preventing such breaches. AI-powered fraud detection systems can analyse various clients’ patterns such as behavior, location, and buying habits to plan a security mechanism. This mechanism receives an alert when it detects an anomaly in spending patterns. Financial institutions can study these alerts and then take corrective actions.
AI has been powering the investment market for some time now and is helping investors make smart decisions. In 2018, the total data-driven investment reached 1 trillion dollars, and it is expected to grow further. Investing is a time-sensitive industry and AI-enabled Real-time dashboards provide advanced predictions to investors. This leads to smart investments and greater ROI. One practical example of the increased use of AI is seen in the Bloomberg Alpaca Forecast AI prediction matrix. This model uses Bloomberg market data to detect patterns in price movements allowing investors make smarter predictions.
5. Personalized banking
Almost every major bank uses a smart chatbot in today’s market. Smart Chatbots are AI applications that get better with every customer interaction and reduces manual call-center workload. Banks are also moving towards offering voice-controlled virtual assistants, like Alexa to offer customized offers and services. These features can check account balance, payment scheduling, check account activities, and more. Additionally, many apps help customers with personalized financial advice and assist them in realizing their financial goals. They accomplish this by monitoring the spending patterns and habits of the customers and then use this data to create a smart financial plan.
AI has great potential in making credit decisions more seamless and efficient. In addition, AI is creating a metamorphosis of organizations across domains and verticals and is becoming sophisticated, smart, and intuitive. While this is an advantage for the industry, it is essential to view AI as more of augmented intelligence rather than artificial intelligence.
The underlying truth remains that AI and ML can lead to optimum results only when they work in tandem with humans. The future of AI is collaborative intelligence, i.e., the growth of AI must be mapped with the human capital. This also means that as AI evolves, our workforce would also need to upscale in terms of skills and capabilities. Coming back to AI and Fintech, a critical need for the future is transactional and account security, especially as cryptocurrencies and blockchains become mainstream. Another scope for growth is in of digital assistants, which will continue to grow, thanks to cognitive technologies. Overall, the future looks promising and it is now the responsibility of companies to implement AI in a smart and effective manner.