Quick guide to BFSI AI & Intelligent Automation

The BFSI enterprise and customer journey

Seth Adler

On South 3rd Street, Philadelphia, sits a historic building featuring Corinthian columns under a triglyph frieze. In 1797, First Bank of the United States opened its Greek Revival doors to the American public. Today, new banks are opening their virtual doors with URL addresses. Technology transforms the way banking, financial services, and insurance (BFSI) meet public need. Further, artificial intelligence is pushing BFSI into an entirely new dimension of change.

Artificial intelligence, the science of simulating human intelligence by machines, offers BFSI the opportunity to improve on and expand customer experience, back-office processes, marketing and security to name a few. This value-add benefits clients and enterprise alike. AI has the potential to decrease corporate spend, which can then be passed onto the customer. Thus AI has the potential to increase customer satisfaction in a day where user experience (UX) matters more than ever.


As customer expectations evolve, so must the customer experience an enterprise offers. Today, people expect quick answers to their questions and a seamless virtual experience. Artificial intelligence is achieving those goals for BFSI enterprises.


Chatbots are the most obvious form of AI use in BFSI. These bots, fueled by Big Data and AI, offer the real-time guidance customers expect in today’s at-your-fingertips culture. But chatbots are more than a simple communication tool. BFSI puts customer data to use by offering products and services that meet a customer’s need before they even ask.

Additionally, AI chatbots recognize when a human consultant is best for a customer service scenario and moves the transaction onto a human encounter. That means clients aren’t struggling with bots that don’t understand their inquiry or going in circles with a pre-programmed script. After all, AI isn’t meant to replace the human touch—it’s meant to augment it.

Big data

Further, machine learning allows for the AI algorithm to increase in accuracy as more data is fed into its system. This ability works both in a largescale and personalized way. Newly collected customer data in BFSI opens a host of new opportunities for marketing. Cookies track movement of customers online. When enough data is collected from enough client sources, algorithms and machine learning can use that information to offer highly targeted products to clients who are the most likely in the market for them.

Banking applications monitor and analyze a customer’s spending habits on an individual basis. As the application learns the habits of an individual, it offers personalized advice, warnings, and products to ensure their money is used to the best of its ability. For example, after closely monitoring a client’s account, the application may recognize that a customer is eligible for an interest-bearing checking account. Then, not only does it suggest a switch, but, upon client approval, it makes the switch in a no-hassle, light touch way.

Speed to consumer

In insurance, claims are processed faster with the use of AI. Custom insurance plans based on a customer’s needs and risk factors are generated in near-real time. AI also works with the Internet of Things (IoT). For example, sensors are available for installation to detect gas and water leaks. When a risk is detected, a chatbot contacts the homeowner to make them aware of the situation and awaits further instruction. Both the insurer and the homeowner benefit from this averted crisis.

Predictive power

AI financial advising monitors stocks and makes suggestions based on a user’s age, financial goals, and risk tolerance. It also processes massive amounts of data to watch the market for investment trends the human eye overlooks.

Examples of AI software for customer experience

  1. PersoneticsPersonetics offers banks an AI solution for increased customer service. Their three products, Engage, Act, and Assist tout the ability to give personalized financial guidance, automate money management, and provide a conversational banking platform. It analyzes customer transactions and predicts customer needs in order to offer a solution that boosts both customer and bank ROI.
  2. EquBotIncluded in EquBot’s claims is the ability to use AI to unearth incorrectly valuated companies ripe for investment. It harnesses the power of IBM Watson and its own proprietary algorithms to pore over news articles, social media postings, and financial statements. It then analyzes and predicts market trends without human bias or overhead.



Tedious, manual back-office processes are accomplished by AI in a faster, more reliable way. AI can accurately analyze contracts, eliminating inevitable human error. Data migration, spreadsheet updating, and report generation are all ways AI saves time and resources in the back office. In turn, human talent is freed up to meet customer need in a detailed, personalized, and immediate way.

Predictive analytics

Underwriters get a boost from AI by saving back-office time and resources by accurately predicting risks. AI’s prediction power is used when screening customer applications for credits and loans. In insurance, AI models create better risk assessments through predictive analytics. For example, worker’s comp underwriters now use AI to analyze past claims in order to predict—and hopefully prevent—future accidents. Financial institutions use predictive analytics to detect and prevent fraud, to score credit, and to approve loans. Instantly signing up for a credit card or loan online is possible because of predictive analytics.

Attended Automation

Bots aren’t just for customers. By deploying AI bots in the back end, employees are able to get their questions answered faster than ever. Instead of examining training modules, accessing clunky intranet search engines, or manually searching through client data, an employee can simply ask the AI-enabled bot to do the research for them.

A hybrid approach

A hybrid approach to AI combines the speed and ease of unattended processes with the human oversight sometimes necessary in attended processes. For example, an insurance company may take advantage of a hybrid RPA model when processing claims. The act of sorting, categorizing, and streamlining claims works in an unattended way, where the decision to deny or approve a claim is a task best left to human oversight.

Examples of AI software for mid- and back-end BPM

  1. UltimusUltimus offers an end-to-end solution that automates and streamlines workflows from the back-, middle-, and frontend. Its low-code AI means that it is designed to be easily programmable and user-friendly by enterprise employees from the c-suite to the seasonal worker.
  2. PegaPega streamlines employee workflows by automating tasks. It marries business and IT departments through AI, breaking up silos that lead to slow solutions and task management.



Compliance in banking is constantly changing, and financial institutions must adapt to regulatory changes in real-time across workflows. Additionally, AI expertly identifies fraud and takes action to mitigate the damage it causes.

Compliance and regulations

A bank’s internal compliance team benefits from AI’s ability to stay on top of internal and external documents that detail upcoming regulatory and compliance changes. That means a compliance officer must read every new updated regulatory guideline, parse through the organization’s infrastructure to see what guidelines are applicable, and make the appropriate changes. AI does the heavy lifting by examining the documents, identifying the applicable regulations, and modifying workflow processes accordingly.

Fraud detection

AI fraud detection applications collect public customer data from across the internet to make sure that banking activities align with a user’s profile. Combined with a financial institution’s internal 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, 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.

The future of cybersecurity

Predictive analytics can help discover data breaches far earlier than the human eye. It can also forecast the when and where of potential cybercrimes, leading to concentrated fortification and detection efforts. While there is still a long way to go in this arena, with the explosion of cybercrime, predictive analytics is shaping up to be a powerful cybersecurity tool.

Examples of AI software for security & compliance

  1. CognizantCognizant is able to spot, label, and control fraud with its cognitive AI solutions. It deploys algorithms programmed with cases of known fraud and trends in fraud in order to identify and stop fraudulent activity.
  2. DeltaconXDeltaconX offers a single solution that brings together compliance efforts under one regulatory platform.


Brick and mortar institutions are being replaced by the ease and convenience of connected devices. Gen-Zers are growing up, getting jobs, and spending money. To remain relevant in the eye of today’s consumers, BFSI brands are deploying AI solutions to meet their customers where they are. By making customized recommendations, speeding up timely processes, and securing customer data, legacy enterprises and startups alike are maintaining relevance in this new era of technology.