How BFSI is Harnessing the Power of AI & Intelligent AutomationAdd bookmark
Banking, Financial Services, and Insurance (BFSI) organizations are racing to adopt Artificial Intelligence (AI) and Intelligent Automation (IA).
Artificial intelligence, the science of simulating human intelligence by machines, and Intelligent Automation (IA), the combination of AI and RPA, are already enabling organizations to reach new heights when it comes to operational efficiency, customer experience and security. However, the BFSI would is just getting started and the AI market in this sector is expected to reach $247,366.7 million by 2026, registering a CAGR of 38.0% from 2019-2026.
How AI and IA are Delivering Seamless Customer Experiences in BFSI
Customer tastes are changing even when it comes to how they bank and manage their finances. The modern banking customer expects seamless service and state-of-the-art, consumerized UX. With FinTech disrupters continuously nipping at their heels, BFSI organizations are unleashing a new wave of AI-powered CX solutions.
Chatbots use Natural Language Processing (NLP), a type of AI that can understand and reply to user requests in the form of free language, to provide 24/7 service. Chatbot not only answer simple questions immediately, but, using predictive analytics, can also predict customers needs and recommend services accordingly.
If a customer has a more complex query, the chatbot will simply redirect them to a human subject matter expert.
Few industries generate more data than BFSI. Using machine learning (ML) and AI, BFSI organizations can now transform massive amounts of customer data into new, elevated user experiences, new services and personalized product recommendations.
Enterprise data and analytics enable BFSI organizations to gain a 360-degree view of their customers based on their digital behaviors, queries and general investment habits. Not only do predict analytics help BFSI recommend target products, it also enables the AI to continuously improve and learn from past interactions.
The power of big data to improve the lives of customers is basically unlimited. For example, these new AI-powered systems can track spending and help customers make better financial or investment decisions. They can even pull data from external sources, such as social media, to predict life changes, such as marriage or childbirth, and recommend new services.
Insurance Claims Accelerated
Thanks to AI, insurance claims are now processed faster than ever and 24/7. Unless there is some sort of major issue, the “touchless insurance claim process” remove th human element entirely and, using AI, can report the claim, capture damage, update the system and communicate with the customer all by itself.
Boosting BFSI Back-Office Business Processes with AI
Few industries generate more data than BFSI. Furthermore, few industries as task-heavy as BSFI as well, especially when it comes to the back-office. This makes the BFSI industry uniquely positioned to benefit heavily from AI, intelligent automation and the like.
For example, at banks, legal teams need to read through several thousands of documents such as NDAs, logistical agreements, or compliance documents every single day in order to identify issues for the bank that may lead to losses. AI-based contract management solutions can scan vendor contracts or employee agreements to identify any glaring issues, saving organizations millions in legal fees.
AIapplications save underwriters time and resources by accurately predicting risks. AI’s to screen customer applications for credits and loans, predicting with great accuracy who is most likely to default or defraud.
Similarly, in insurance, AI-powered, predictive analytics tools deliver improved risk assessments. For example, worker’s comp underwriters now use AI to analyze past claims in order to predict and prevent future workplace accidents.
Using AI-power, virtual assistants, employees are able to get their questions answered in real-time.
A hybrid approach to AI combines the speed and ease of unattended processes with the human oversight sometimes necessary in attended processes. For example, AI bots can take care of the act of sorting, categorizing, and streamlining insurance claims. However, the decision to deny or approve a claim is left to humans.
Fortifying Security & Compliance Efforts with Cognitive Technologies
Every day, cyber criminals and other nefarious actors are becoming increasingly sophisticated. BFSI organizations must keep up to prevent financial losses and protect their customers. Additionally, new AI-powered help BFSI companies keep up to date with and maintain compliance with evolving, global regulatory requirements.
Compliance and regulations
Internal compliance teams benefit from AI’s ability to stay on top of internal and external documents that detail upcoming regulatory and compliance changes. Instead of a compliance officer manually reading every new regulatory guideline and figuring out where and how it applies to the organization, AI does the heavy lifting. These days, IA can pretty much handle the entire compliance lifecycle from scanning the documents to identify applicable regulations to modifying workflow processes accordingly.
AI fraud detection applications collect public customer data from across the entire internet to identify who is a real customer, and who may not be. Combined with a financial institution’s internal customer data, a high level of accuracy is achieved in spotting fraudulent activities in real-time. Additionally, false flags are reduced. For example, in the past, if a credit card holder swiped their card from the other side of the country, the card ran the risk of being locked by the financial institution. With today’s AI tools and predictive analytics, a bank may have access to a customer’s geolocation, transaction history such as airline tickets, and social media posts regarding future vacations, preventing false flags, and ultimately, damage to the bank/client relationship.
Not only can predictive analytics and AI pinpoint data breaches far earlier than the human eye, they can also forecast potential cyber crimes before they even occur. At this point, proactive defense or active threat hunting is typically leveraged to predict insider threats as predicting external threats is still a work in progress.