The digital marathon that began with the emergence of the internet and has led organizations through numerous stages of digitalization has reached the Financial Services Industry’s Artificial Intelligence (AI) phase. The rise of AI is upending the laws of physics in the sector, loosening the ties that have kept the elements of conventional financial institutions together, and creating space for new ideas and operating models.
The study of artificial intelligence, or AI, focuses on building intelligent machines that function and carry out jobs just like people. These machines possess the capacity to learn, organize, and understand data to base predictions on it. Consequently, it has developed into a crucial component of technology in the Banking, Financial Services, and Insurance (BFSI) Sector, transforming the way goods and services are provided.
Why do banks use AI? “why now?”
The standard of goods and services that the banking sector provides is changing due to AI. It has not only improved user experience and created better ways to handle data, but it has also sped up, streamlined, and redefined conventional procedures to make them more effective.
Data has evolved into an organization’s most valuable asset due to the availability of technology like AI. More than ever, banks are aware of the cutting-edge and economical solutions AI offers and realize that, despite its importance, asset size will no longer be sufficient to create a successful company on its own.
Instead, the ability of BFSI organizations to leverage technology to harness the power of their data to produce innovative and individualized products and services is now used to gauge their success.
How is AI disrupting banking?
Data increase (Big Data): The Big Data market has experienced tremendous growth, which has greatly impacted the banking sector due to changing consumer expectations. Customers now interact with their banks on a more digital level. In addition to the traditional structured data, such as transactional data, organizations now gather large volumes of unstructured data through their customer service, social media platforms, and other means of data collection, such as emails, text and voice messages, images, and videos.
Banks are now able to provide more individualized services because of big data. A 360-degree view of the customer’s relationship with the brand, comprising basic personal information, transaction history, and social media interactions, is used by banking organizations to guide decision-making.
Infrastructure availability (fast computers, hardware, software, cloud): The development of cloud technology, along with high computational resources and infrastructure availability, provides for efficient scalability and quick processing of massive amounts of data. This indicates that businesses are more prepared than ever to use AI.
Bank regulatory requirements: To fulfil their regulatory commitments, banks are under intense scrutiny from regulators who want them to submit correct reports on schedule. Processes for regulatory compliance demand the gathering of data from diverse source systems. By automating the data gathering procedures, boosting the speed and quality of decisions, and enhancing the organization’s ability to fulfil regulatory compliance needs, AI-driven solutions present an opportunity to address some of the difficulties in today’s financial systems.
As artificial intelligence advances, financial organizations’ front and back offices will undergo a profound transformation. The rise of AI will necessitate significant modifications to the global financial market structure as well as alterations to long-standing rules. To help banks become more future-ready, this transition presents an opportunity for compliance teams to invest in new technology deliberately.
Competition: To offer their customers the finest services possible, banks are always competing with other businesses in the sector and, more lately, with FinTechs. As businesses use cutting-edge technology to gather the enormous quantity of data they already have, technology has emerged as a differentiator in this market. As a result, banks are utilizing AI to enhance their present service offerings, launch new ones, and give clients a more individualized experience.
For businesses to fully take advantage of the benefits that AI has to offer, these factors are continually changing and presenting new opportunities and values. The BFSI industry is perfectly situated to take advantage of this disruption and make progress on its path toward digital transformation.
Applications of AI in the banking industry
We have already observed the use of this disruptive technology in several areas of banking services. The BFSI industry has seen the largest influence from AI in the use cases listed below.
Chatbots: Natural Language Processing (NLP) and AI-powered chatbots connect and interact with customers around-the-clock to improve online dialogues. Chatbots are now able to assist with a variety of tasks, such as opening new accounts and forwarding complaints to the proper customer care departments, in addition to providing clients with the standard answers to their inquiries to guide them through their account data.
Fraud Detection & Prevention: Up until recently, banks depended on anti-money laundering (AML) transaction monitoring and name screening systems based on old rules and producing many false positives.
Enhanced AI components are being added to the existing systems to identify previously undetected transactional patterns, data anomalies, and suspicious relationships between individuals and entities in response to the alarming rise in fraud-related crimes and constantly evolving fraud patterns.
As opposed to the conventional reactive method of fraud detection, this enables a more proactive approach where AI is utilized to stop fraud before it happens.
Customer Relationship Management: Managing relationships with customers is crucial for banks. Banks now offer more individualized, round-the-clock services to each of their customers, including the ability to connect to banking apps using facial recognition and voice commands.
Additionally, banks are using artificial intelligence to analyze consumer behaviour patterns and segment customers automatically, enabling targeted marketing and better customer interaction.
Predictive Analytics: The emergence of Machine Learning (ML) & AI has made it possible to foresee and predict events accurately. The performance of the models has improved exponentially as data collection has increased, gradually reducing the amount of human intervention needed in the process. Revenue forecasting, stock price forecasting, risk management, and case management are all areas where data analytics and AI are used.
Credit Risk Management: Financial organizations are required to provide more dependable models and solutions as regulators continue to place a strong emphasis on risk management oversight. Especially in the Fintech and Digital Banking markets, the application of AI in credit risk management is growing in popularity.
By utilizing data to anticipate the likelihood of default, AI is utilized to assess the creditworthiness of the facility borrower, helping to increase the precision of credit choices. As a result, the market is shifting away from expert judgment and toward insights-driven lending, which maximizes the rejection of high-risk consumers and minimizes the rejection of creditworthy customers while also lowering credit losses suffered by financial institutions.
Conclusion
Artificial intelligence technologies have a tonne of potential applications in the finance sector. They all strive to improve processes through automation and eliminate the need for human action and labour.
The uses mentioned above of AI demonstrate its significant potential to stabilize the whole financial sector and boost global economic growth. Financial organizations can benefit greatly from using AI-based tools.
