AI for Banking, Insurance, and Financial Services
AI for Banking, Insurance, and Financial Services
AI will naturally alter banking, insurance, and other financial services over the next few years.
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Artificial Intelligence and Robotic Process Automation together became the biggest technology revolution for industries nowadays. The applications that have cognitive capabilities deal with creating software with self-learning capabilities that are able to recognize their environment and solve problems like humans.
Artificial Intelligence will naturally alter banking, insurance, and other financial services over the next few years, impacting customer service, fraud mitigation, loans and credit scores, and investment advisories. These will all use AI and Machine Learning to deliver an excellent customer experience and efficient operations for banks, insurance companies, and the financial sector of the companies.
Insurance companies have a different kind of adoption of AI and Robotic Process Automation (RPA) than Banks or Financial Services. Insurance companies are more serious about using AI and RPA for customer support.
The major objectives of implementing AI are personalized customer experience, having error-free, back-end processes that run without human intervention and faster turn-around time. The security and compliance appear to be the last priority.
The e-commerce and social media apps have already set the bar incredibly high, so users are coming with very high levels of expectation in terms of usability from banking and insurance apps. That brings us to the impact; AI will have to be on job to meet customer expectations.
The traditional approach will not be able to deal effectively with the volume, velocity, or variety of data being generated. Data has to be collected from a different source, which is in disparate formats.
The new approach to BFSI is that decision-making often goes beyond the traditional consideration of speed, functionality, ROI, etc. In insurance — underwriting or personal financial advice — there is a sizeable element of subjectivity. Feeding these rules in AI systems may be more difficult than we think. To address this, IEEE has been working on Ethically Aligned Design, which necessitates that transparency, accountability, and algorithms will have to be considered right at the time of design.
Implementing Robotic Process Automation and Artificial Intelligence will result in job cuts, but it is temporary. The new opportunities will get created, which requires highly skilled resources. The repetitive and routine task will be performed by automation, and people can focus on more critical and skillful tasks.
The real concern of using AI is its lack of maturity in standards. This creates silos of options, which makes vendor identification a major task. Another issue is that Machine Learning depends on a huge amount of data — respondents have found it challenging to train their Deep Learning systems with less amount of data. Appropriate talent or skilled resource availability is also a bit of a challenge for adopting AI.
High-Level Business Objectives of Automation
The primary business objective using AI is to offer a more proactive customer experience by automating back-end processes to reduce human error and improve the TAT (turnaround time) for manual processes. A robot can work 24/7 without human intervention, which results in improved SLA and customer satisfaction.
The third most important business objective is to monitor all processes and data for regulatory compliance and security. The top fourth business objective with AI is to use it for marketing wherein they’d like to track consumer behavior so that customized products can be offered to them.
Customer Support: Offer more proactive and personal customer experience at a lower cost
Backend BPM: Reduce human error, improve turn-around-time of routine back-end manual processes
Marketing: Track consumer behavior and offer customized products
Security and Compliance: Monitor processes and data for regulatory compliance, anti-money laundering (AML), and risk management.
Different AI Flavors
Customer Support and Marketing
Self-learning programs for intelligent conversations with humans over chat or audio; Available 24×7 and very easy to use but require a long time for training.
Robo-Advisors for Financial Products:
Online platforms that use algorithms to offer financial advice, re-invest dividends, automatic portfolio creation and re-balancing of the portfolio etc. This requires minimal to zero human intervention.
Personalized Financial Services:
Robo-advisors to monitor customer goals and suggest stocks or bonds to buy/sell; Gives personalized attention to customers irrespective of their risk appetite.
Intelligence added to mobile wallets for smart services like chat, booking of bus tickets, cab, events, movies, utility bill payments, etc.
A branch of AI to enable machines to detect human emotions with advanced facial and voice recognition technologies.
Security and Compliance
Fraud Detection and Prevention:
Minimize the need to add continuous manpower to detect and block security attacks; These platforms use machine learning to automate the process.
Use AI to examine lengthy documents and flag potential issues in seconds, which would otherwise take many hours.
A new segment of Artificial Intelligence companies that specialize in helping companies remain compliant, e.g. ensure no document is missed out while filing something, do risk mitigation by monitoring customer behavior from empirical data.
Robotic Process Automation:
The use of software robots to take over high volume, back-office processes, and repetitive tasks to save time, enhance efficiency, and increases accuracy.
Using AI for faster and more accurate risk assessment and pricing to measure the risk exposure and determine premium to be paid by a customer.
AI for high-frequency trading where inputs are taken from multiple financial markets to make investment decisions in milliseconds. Reports suggest that over 70% of trading worldwide today is being managed by algorithms.
AI to guide investors on stock picking decisions. It can help cover more companies in exchanges all over the world, do their research and portfolio management.
AI to save hiring manager’s time in various recruitment processes e.g. engage with new recruits, shortlist resumes from social media sites, pre-screen candidates over chat, determine candidate drop out chances, etc.
Prerequisites for Implementing AI
Before AI can be applied to BFSI data, they must first be digitized and made searchable.
Centralized and Clean Data:
Companies need to centralize their data from different servers and clean it before applying AI on it.
Validate Algorithm Outcomes:
Algorithms used to process data must be clean in order to deliver the right outcome. Since they’re very complex, understanding their inner working can be a challenge. That’s why their outcome must be continuously monitored and validates.
Have to Remove Algorithm Bias:
Outcomes from algorithms should not be in favor of a particular outcome, which is why it’s essential to have the right data and do continuous monitoring of output.
Need Right API Standards for Data Sharing:
The data sharing and integration has to be proper. So it’s critical to ensure that the right API standards are used to avoid data from getting compromised.
Benefits of AI for BFSI Industries
More Accuracy and Predictability:
AI’s primary goal is to increase accuracy and predictability of outcomes and reduce human errors in business processes, particularly manpower-intensive ones.
Availability and Scalability:
AI-based solutions can work 24/7 without taking breaks or getting tired. They are scalable because they use deep learning technology to continuously improve themselves through self-learning.
Digital Fraud Detection and Prevention:
UPI transactions and digital payments are growing month-on-month, it will lead to an equally big spike in digital frauds. AI technologies can monitor huge volumes of digital transactions to identify and prevent digital frauds.
Understand Customer Behavior:
As customers go online, AI will be required to analyze customer behavior and improve their experience.
Maintain Regulatory Compliance:
Negligence in security measures could lead to financial losses.AI can help companies to analyze data to detect any regulatory deviations to stay on the right side of the law.
AutomationEdge robotic process automation solution also has Cognitive capabilities like Artificial Intelligence, Machine Learning, and Natural Language Processing to enable your applications and processes with intelligent automation.
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