Artificial Intelligence: What does it hold for the BFSI industry?

In the previous blog, we briefly discussed AI in healthcare and how AI is transforming the industry. We talked about the benefits and impact of artificial intelligence in healthcare and how it is helping to mitigate the industry’s serious challenges. This blog discusses in detail the impact of Artificial Intelligence on another industry – Banking, Financial Services, and Insurance (BFSI).

Like healthcare, the BFSI industry is critical to individuals, families, and companies. BFSI companies rely heavily on decision-making and problem-solving, and the introduction of AI has helped in making the industry more customer-centric, efficient, and secure. Per a report by McKinsey, up to 55% of the work in insurance company functions like actuarial, claims, underwriting, finance, and operations could be automated over the next decade.

Furthermore, per another report by Business Insider Intelligence, almost 80% of banking institutions acknowledge the potential benefits of artificial intelligence. The report adds that AI applications can drive potential cost savings of an estimated $447 billion by 2023. For instance, in banking alone, AI can help save costs in the front office with conversational banking, the middle office with efficient fraud detection, and the back office with underwriting.

AI software solutions are making significant headway in the BFSI sector and help an organization become a data-driven enterprise. Today, AI is implemented and deployed for fraud detection & prevention, anti-money laundering, customer relationship management, data analytics & prediction, and other crucial functions. Moreover, AI ensures seamless customer identification and authentication, provides data-driven insights and recommendations, and improves customer service and end user support.

Here are some use cases of how AI is helping the BFSI industry:

Chatbot/Virtual Assistants

AI-powered chatbots and virtual assistants are one of the best examples of practical AI usage in the BFSI sector. Chatbots are becoming significant, particularly in improving customer experiences. For instance, a Uberall report found that 80% of chatbot user experiences are positive. Moreover, unlike humans, chatbots and virtual assistants can operate 24×7, collect data to identify patterns, and are less expensive than human customer services representatives. They also can offer personalized customer support and recommend suitable financial services or products.

Erica – Bank of America’s AI-powered chatbot – is one of the best examples of AI implementation in banking. Launched in 2018, Erica has become one of the most accessed virtual assistants in the banking industry. For instance, Bank of America last year announced that Erica is accessed by 98% of the bank’s customers and surpassed 1 billion interactions.

AXA, the multinational insurance company, has deployed two chatbots – Emma and AXA Chat – to improve their customer services. Launched in 2019, Emma is the company’s in-app chatbot and provides personal services like investment monitoring, 24×7 emergency services, and online insurance purchases. On the other hand, AXA Chat is a more accessible chatbot that offers clever sales and customer-focused assistance.

Security and fraud detection

McKinsey’s 2022 Digital Payments Consumer Survey concludes that 9 out of 10 American customers use some form of digital payment. It adds that digital payments penetration increased to 89% in 2022. This promising growth has made the industry highly vulnerable to cybercrimes and fraud. Per Accenture, the cost of cyberattacks was the highest in the banking industry and amounted to $18.3 million annually per company. A study by Coalition Against Insurance Fraud further states that insurance fraud costs $309 billion a year in the US.  This equates to almost $1,000 for every single U.S. citizen. Furthermore, the FBI estimated that the total cost of insurance fraud for areas besides health insurance is more than $40 billion annually. Fortunately, AI can help the BFSI industry in mitigating this risk.

AI combined with machine learning can assist in identifying fraudulent activities and sending timely alerts to customers and banks. Trained AI algorithms can identify patterns and make real-time predictions which help in improving online finances, tracking system loopholes, and mitigating risks. For instance, algorithms can sort through large volumes of transaction data – historically labeled as fraudulent or non-fraudulent – to learn how to flag suspicious activities and fraud possibilities.

Here are some use cases of AI for fraud detection –

• J.P. Morgan Chase, one of the biggest financial services companies in the world, has doubled its fraud detection rate with proprietary AI algorithms designed to flag unauthorized transactions. The company developed (and now uses) an “Early Warning” security system that harnesses the power of AI to detect Malware, Trojans, and other advanced persistent threats. The system utilizes big data and deep learning algorithms for early detection of phishing emails and other malicious payloads targeting bank employees.

 

• HSBC bank also partnered with a machine learning software company and developed an AI-powered anti-money laundering solution. The solution can identify patterns in historical data that suggest money laundering and detect fraudulent patterns to alert staff that they should block payments. Furthermore, it analyzes the source and destination of payments to identify deviations from normal behavior. The solution also safeguards the bank from false positive alerts and allegedly reduces false positive investigations by 20% without relaxing compliance standards.

 

• In 2022, the Canadian Life and Health Insurance Association (CLHIA) announced the launch of a new anti-fraud initiative to enhance the benefits of fraud detection and investigation capabilities of the industry. It collaborated with a global technology provider to develop an advanced AI model for analyzing industry-wide anonymized claim data.

 

• One of the largest banks in Denmark, Danske Bank implemented an AI-power fraud detection algorithm to boost their fraud detection capabilities. The tool increased the accuracy of fraud detection by 50% and reduced false positives by 60%.

In 2021, more than 2500 cyber incidents were reported worldwide in the financial industry – averaging close to 7 incidents per day. There is clearly an urgent need for ramping up security and fraud detection in the BFSI sector, and AI seems like a perfect tool to fulfill the same.

Market trend predictions

BFSI is a data-driven sector that relies actively on data and market trend predictions. The banking industry records billions of transactions daily and has humongous amounts of data stored on its servers. AI can help BFSI companies to process these large volumes of data to predict current and upcoming market trends, market sentiment, currency rates, and stock prices. For instance, many banks and financial institutions implement machine learning techniques to analyze the market and recommend investment options to their valued customers.

With AI in place, companies can predict trends like drops in the stock market. They can quickly analyze the data and gather insights into the effect of the (stock value) drop on the companies’ portfolios. This application of AI proved highly beneficial for the BFSI industry during the Covid-19 pandemic when digital services took center stage.

In the insurance industry, AI can be leveraged for claims forecasting and prediction. AI-based systems can use machine learning to analyze numerous data patterns (including individual characteristics) to offer better accuracy and predict payments at individual and group policy levels.

AI can also provide insights into customer retention and churn prediction. The ROI opportunity to optimize customer retention is significant. AI can predict possible churners and make an additional impact with uplift modeling. The latter identifies potential churners likely to respond positively to marketing messages and helps an insurance company direct its efforts in the right direction.

Artificial intelligence is emerging as one of the most influential technologies in the banking sector due to its range of real benefits. From improving security to personalizing user experience, AI is a perfect tool to drive BFSI businesses today. As per a report by Allied Market Research, global artificial intelligence in the BFSI market is expected to register a CAGR of 38% from 2019 to 2026 and reach almost $247,367 million by the end of the cycle. Another report by Global Market Insights states that AI in the BFSI market size is expected to exhibit a CAGR of 20% during 2023 -2032. It was valued at $20 billion in 2022 and is projected to reach $100 billion by 2032.

While the scenarios explained above are a testament to the remarkable power of artificial intelligence, a critical point is that successful AI necessitates a strong data foundation.

A strong data foundation is a prerequisite to successful AI since any AI application is only as good as the quality of data upon which it is based. For instance, an AI algorithm trained on bad data can lead to bad consequences for your business. Take the example of Zillow, an online real estate marketplace, that suffered losses of nearly $500 million and had to shut down a business unit because its AI application Zestimate was fed and trained with bad data. Building a roadmap to AI enablement without solidifying your data strategy is a recipe for failure, especially in an industry like BFSI which handles highly sensitive data.

Celsior can help you solidify your data strategy and provide you with a roadmap to enable artificial intelligence. We can help you improve the quality of your data, eliminate data silos, and develop data quality practices and processes to enable innovative use of AI and ML.  With that solid foundation, AI can help give you a competitive advantage, reduced costs, and improved customer experiences.

Learn more about our data and AI capabilities here: https://celsiortech.com/data-insights/ In the next blog – we will discuss the impact of Artificial Intelligence on the Manufacturing industry.

ABOUT THE AUTHOR

Shobhit Kulesh
Celsior