How Artificial Intelligence is transforming the banking and insurance sector.
Guest Blogger: Aruna Pattam, Head – & Data Science, Asia Pacific & Middle East, HCL Technologies
Artificial Intelligence () is one technology that’s going to change the way banking and insurance will operate.
Consumers are increasingly turning to digital-only banks, and even the traditional banks have started to offer more online services. It has been predicted that the potential cost savings for banks and insurance firms through applications is estimated to be around $450 million.
can help streamline their processes, make smarter decisions, and manage customer service requests with fewer resources. also can play a major role in risk management by preventing fraud and fighting money laundering in real-time. There are many other different ways that can be used to improve the banking and insurance industry.
In this blog post, we will explore some common applications of in Banking and Insurance.
- Key challenges faced by banking and insurance industry
- How can help
Key challenges faced by banking and insurance industry:
Banks and insurance firms are experiencing a dramatic evolution in their business ecosystems triggered by technological innovations and changing consumer behaviours. Changing lifestyles, online shopping, big data, technology advancements have made interactions across organizations and individuals more real time.
Below are some of the key challenges that are faced by the firms today:
#1: Customer expectations: Customer’s expectations are on the rise as more and more people access banking through their smartphones, tablets, laptops etc.
#2: Digitisation: Digitisation is disrupting the way traditional institutions operate their business and how the services are provided.
#3: Competition: Emergence of FinTechs and other big technology companies entering this market has made it more competitive.
#4: Regulation: Regulatory requirements require the firms to constantly change their ways of operations to be compliant.
#5: To Stay relevant: Leading firms has started making technologies as integral part of their business. This requires others to be also up-to-date with the emerging technologies to be competitive and stay relevant.
How can help:
By using and techniques, we can automate tasks and predict outcomes thus reducing costs and improving service. The possible fields of application of can be broad depending on the type of data involved such as data, images, text etc.
Let us look at some common applications of . I have included a short video that briefly illustrates the common uses of in banking.
#1: Process Optimisation
Banks’ today are looking for ways at lowering operational costs and drive profitability. This involves both front-end and back-end activities. Claims and Mortgage processing are the heavy areas within banking and insurance where could really drive efficiencies.
In Claims processing:
Claims processing is a part of the consumer’s journey with insurance. Whether it be claimsed from car accidents, personal injury, and much more; an insurer will go through all stages to make sure that they are either accepting or rejecting your claim.
By using ’s chatbots, the customer can provide the details about the claims, which can then be verified against the data stored in the systems. If the claim is found valid, then it can be automatically processed and approved. If it is deemed to be complex, then those claims will be passed on to the claims officer to process.
Also, by using ’s , we can analyse large volumes of data about policy holders, their habits etc in order to offer more customised products in real time.
In Mortgage processing:
Mortgage processing is the processing of the mortgage applications. A mortgage processor typically collects the individual’s details along with the financial information, and verifies that all the needed documentation is in place before the loan file is sent to underwriting.
By using ’s machine vision, we can digitise all the paper documents including handwritten loan applications. Once digitised, we can extract the contents automatically to classify documents such as payslips, bank statements, valuation documents and tag them, so we can easily search and find the right documents. We can then use models to identify and recognize patterns and make sense of massive data such as Bank statements, transaction history etc.
Also, by using ’s computer vision and , we can automatically compare and verify names, addresses, loan amount or terms across various documents. We can find matches and mismatches and notify the loan processor.
In Customer Service:
The bank’s customer service relates to its customers’ banking experience. It includes services such as opening an account, deposit and withdrawal of money from an account, ATM card usage, fund transfers between accounts or accounts at different banks etc.
can help by using custom-built intelligent chatbots to streamline tedious customer service processes to automatically solve simple customer requests and prioritise tickets based on sentiments for routing to the right team.
Chatbots can also be used to send notifications to consumers, provide balance data, suggest how to save money, provide credit report updates, pay bills, and help customers with simple transactions.
Operations function within a bank is responsible for the smooth running of all aspects of banking operations and includes customer service operations, call centre operations, back-office support and ATM operations among others.
can help by using techniques, to process large quantities of data in a short period of time thus increasing efficiency and accuracy resulting in reduced operational costs.
For example, banks can introduce -based invoice capture technologies to automate customer invoices and use accessible billing services that remind customers when it’s time for them to make a payment.
#2: Credit Risk
Credit risk management is the process of managing credit exposure, which is a type of financial risk. Credit risk is the possibility that a counterparty will default on his or her contractual obligations toward you. Traditionally if someone is interested in getting a loan, we check their income, bank account history and their repayment ability. Not all of them have this history such as people entering the workforce, new migrants etc and are often deprived of the loan.
Using / technologies, we can process thousands of data points from their digital footprints such as social media, browsing behaviour, Geolocation, and other data to build a credit profile for these borrowers.
This way we are not only able to extend the loans to all parts of the society but still be able to reduce the overall risk to the loan.
#3: Fraud and Money Laundering
Banking fraud is deliberately misusing or abusing banking services in a dishonest way to obtain funds illegally. Fraud can be committed by customers, bank employees, and third parties such as criminals who hack into banking computer systems. Traditionally businesses relied on rules-based methods to block fraudulent payments. But with increased digitisation and more services are being offered means it also opens up for more fraud.
When it comes to fraud decisions, we need to be super-fast, accurate, efficient, able to scale quickly, and economical way of detecting fraud. All of these can only be achieved using and technologies, by running hundreds of thousands of queries and assessing individual customer behaviour as it happens with ‘normal’ customer activity, so we can spot anomalies. All of this happening in real-time.
In Money laundering:
Money laundering in banking means the process of making illegal money or money collected from any kind of crime, look like legal money which can be used to make purchase transactions etc. One of the most popular use cases in money laundering is the reduction of false positives in the generation of fraud alerts. Machine learning can be used to solve this problem.
After the screening engine has generated alerts, can be used to take the alerts and run another round of scoring – this time using the historical alert generation & processing data – and classify the alerts into different priorities (critical, high, medium, low). This helps the alert managers to prioritize the volume of alerts raised by the screening system.
can also be used for name screening, transactions screening, transactions monitoring and can result in significant operational cost savings and can also help the alert managers to focus on genuine alerts.
#4: Trading and wealth management
Trading in banking is buying and selling securities for the banks’ own account and includes underwriting, dealing, and financing. Wealth management is about providing comprehensive investment advice and services to customers.
Banks are increasingly using algorithms to device new trading and investment strategies. can be used to create models that could predict how long it might take to calculate the ROI for each trade using historical data recorded by the human analysts from the investment firm. This can help in improving the overall time taken for the risk prediction process.
Robo-advisor is a type of online wealth management service that provides automated, algorithm-based portfolio management advice. -powered tools can help traders streamline the account opening process and advise them on scaling their portfolios. This could include developing a financial plan, advising on planned home purchases, retirement, protection needs, state planning, etc.
Trade channel optimization:
Trade channel optimisation or simply trade solutions is about making trading simpler and more efficient for institutions.
can be used to get data from the exchange, combine it with other data and create -based models that can help traders/portfolio managers to decide when and what to trade. helps in understanding market dynamics more clearly by using on volumes, open interest and various other data points. This can be used to optimise trade according to price and from which channel to process the payment more quickly.
Stock market is a public market where buyers and sellers trade company stock.
and can be used in stock markets to gather unbiased information, data crunching, data classification, stock analysis, and pattern recognition. can also be helpful for asset managers and hedge funds. The -based software can look for non-obvious connections, news, lookahead bias, and any other online data that might affect investment decisions and prevents catastrophic losses.
Marketing is an organised way of identifying the needs of the customers and serving them. The organisation decides what service it should offer to the customers so that they are satisfied and get maximum out of their purchase.
Using various solutions such as transcribe, sentiment analysis, keyword matching, event extractions, etc. we can answer questions such as how can I keep my customers happy? What are people saying about me? Who is interested in my product and what’s happening with my competitors?
Also, smart chatbots can be used to help customers to interact with financial companies better. With the help of smart apps, the customers may automatically track their spending, plan their budget, and get accurate saving and investing suggestions.
#6: Security and compliance:
Security is about the protection of information technology systems from unauthorized access or attacks by hackers and viruses.
can help to find breaches in the security, such as analysing documentation for account registration, detect issues within accounts and more. For cybersecurity, we can use unsupervised techniques to establish a ‘pattern of life’ for every user and device within the organisation and detect anomalies and legitimate threats.
Also, can help with data protection and privacy. Banks store a lot of personal and sensitive data about their customers. Now algorithms can be used to sniff through the data, identify where they are stored, whether it is encrypted or not and then make changes as needed.
The banking system is a highly regulated business, which means that banks have to follow all the rules and regulations set by regulatory bodies. The main aim of these rules and regulations is to make sure that the money in the bank is safe.
and Robotic Process Automation (RPA), can be used to deliver regulatory change automation in real time across the enterprise. -based solutions can scan and analyse millions of lines in regulatory content including legal documents, commentary, guidance, legal cases to spot applicable requirements much faster and to enable compliance.
In the coming years, is going to be a major force in the banking and insurance sector. It will change everything from how the firms operate internally to what they offer their customers online and on mobile devices.
is not the future of banking, it is the present.
As more and more data become available, new technologies like quantum, edge and will continue to transform the market.
Success requires a holistic transformation spanning multiple layers of the organisation and need to pull together people, processes and data and have them all work collaboratively.
For these firms ensuring the adoption of technologies across the enterprise is no longer a choice but a strategic imperative.
What do you think? How do you see impacting the way banking operates in future years? Have you used any forms of at your bank? Please share your thoughts below!
About the Author:
Aruna Pattam heads the & Data Science practice at HCL Technologies for Asia Pacific and Middle East region. She has spent the last 21+ years delivering high impact solutions using data analytics, , and analytics platforms.
Aruna is a thought leader, speaker, content creator and regularly shares informative blogs, storytelling videos etc. aimed at demystifying and detailing the ever-expanding scope for business transformation.