Leveraging the Power of AI and Data Sciences in the Financial Services Sector
Advanced technology, like artificial intelligence (AI) and data sciences are being increasingly deployed in the vast financial services industry. The industry includes banks, cooperative credit societies, lending institutions, investment bankers, insurance companies, pension companies and other institutions involved in financial services. The financial services sector is among the largest spenders on advanced technologies and experiencing exponential growth.
AI and Data Sciences in the Financial Services Industry
AI and data sciences are revolutionising how consumers and institutions can access and manage their finances based on massive data sources and analytics referred to as ‘big data’. These advanced technologies can improve various processes for both businesses and consumers. Here is how the power of AI and data sciences is leveraged to transform the financial sector:
Creating Accurate Insurance Policies
The insurance industry gets impacted by AI-enabled applications significantly. Insurance providers and insurers move from traditional claims management and adopt digital workflows based on an analytics-driven approach. AI-based applications can transform the submitted data into actionable structured data and suggest better policies based on calculated risk in seconds. It includes automating claims processing and offering unique digital services to increase customer satisfaction.
Fraud Detection, Transaction Screening and Monitoring
Credit card fraud, false insurance claims, loan application fraud, etc., are a few among financial frauds. It is a concern for all financial institutions from large global institutions to small partnerships. Financial institutions can use specially designed AI algorithms to prevent or detect fraud. These applications are advanced enough to detect anomalies or unusual patterns and prevent fraud. Humans may skip small discrepancies and frauds may get unnoticed. AI algorithms thrive effectively and at scale.
Credit Intermediation and Assessment of Creditworthiness for Improved Lending
Each individual has different financial habits that reflect in their financial activities. Using AI and data analytics, financial institutions can detect consumer financial behaviours and patterns efficiently. Insightful data helps financial institutions to approve a genuine credit application. Since the number of borrowers is on the rise, there is a need for a quick approval process. It is possible with advanced technologies. It also reduces the chances of biased decision-making. Hence, AI and data sciences enable lending institutions to make more accurate and quicker decisions by differentiating high-risk and creditworthy individuals.
Technology has taken personalisation to the next level. Using AI, financial institutions can get the relevant data to provide more personalised financial products, and services as per the consumer's behaviour. This hyper-personalisation will be a dominant trend in the upcoming years. Providing exceptional customer service is paramount to ensure success in the financial sector. Sophisticated AI-based techniques are ideal to satisfy customers with insights generated from customers' financial behaviour, social media interactions, previous financial transactions, feedback and opinions.
Algorithmic stock trading or automated trading system is an automated setup that uses complex mathematical formulas to decide on buys and sells. Typically, humans trade manually by analysing historical and current market situations and trends. Vast data can be analysed accurately using AI-based applications. These would be swift and highly accurate data-driven trading decisions to increase the odds of profits.
Financial institutions take care of continually changing regulations by financial market regulators. It is part of risk management and crucial for financial institutions. Risk management is crucial to avoid threats that can impact their functioning or profit. It includes regulatory risk, credit risk, political risk, legal risk, market risk, inflation risk, and others. The manual system is inefficient to assess such risks and implement solutions. Technology has paved a new way for addressing risks and changes in regulation. Institutions can use Intelligent Process Automation (IPA) to assess big data in real-time, quickly and accurately which can help them complete the compliance process.
Quick Processing: A common use case of advanced technology is process automation. It allows service providers and customers to save time, cost and effort. Replacing manual systems for repetitive tasks with automated processes results in improved productivity. Also, it raises the quality of the services and operations by eliminating the human error factor. Process automation can be applied to various services including, KYC (Know Your Customer), chatbots, call-centre, claims processing, marketing optimisation, etc.
Thus, in the wake of a changing way businesses function and constant pressure to remain innovative, the financial services sector is leveraging the power of AI-enabled solutions and data sciences. AI systems and data sciences help institutions make predictions, recommendations or decisions based on big data. It ensures providing the best financial solutions for consumers using advanced technology.