Original article was published by venkat vajradhar on Artificial Intelligence on Medium
Future Of Artificial Intelligence In The Banking Sector (Part-2)
Improved operations, efficient cost management vs. focus on profitability:
Banks essentially have to make a profit to survive, and today, banks face significant pressure on their margins. Regulators and their persistent focus on transparency make several businesses unprofitable.
AI technologies enable banks to bring more efficiency to their operations and manage costs. Robotic Process Automation (RPA) and Intelligent Process Automation (IPA) are immensely helpful here. Parsing of financial deals is just a matter of a few seconds, thanks to AI.
They can also help manage contracts and act as brokers, simultaneously taking over routine tasks, thus improving productivity and efficiency. All this transforms into increased revenue, reduced costs, and a boost in profits. Robotic automation of processes can reconfigure the financial sector and make it more humane and intelligent. Automation of about 80% of repetitive work processes helps officers dedicate their time to value-added operations that require a high level of human intervention like product marketing.
What we need now is not just empowering of banks by automation, but making the entire system intelligent enough to beat the newly emerging FinTech players. This has prompted a lot of banks to use software robotics to ease the back-end process and achieve a better functional design. SBI plans to institute an ‘Innovation Centre’ to explore RPA, which can help in making internal banking processes more efficient.
Intelligent Character Recognition System
This system has been used by some foreign banks to recognize, extract important information from old loan applications, lease agreements, and feed it to a central database that can be accessed by everyone. It can help in costly and error-prone banking services like claims management by drastically reducing the time spent in reading or recording client information.
For instance, JPMorgan Chase’s COiN reviews documents and extracts data from 12,000 documents (which, without automation, would require more than 360,000 hours of work) in just seconds.
A minuscule percentage of the Indian population has an idea of credit. Even to this day, applying for loans is considered a hassled process. It is also an annoying task for banks to analyze an individual’s creditworthiness due to the lack of credit history.
The use of Big Data and Machine Learning to analyze spending patterns and behavioral data of a customer over 10,000+ data points can help banks have an insight into the customer’s creditworthiness. This also helps in giving pre-approved loans to a huge range of customers without the need for paperwork and allows self-employed and students (as they are out of the financial fold) to obtain credit. In the case of SME and corporate loans, AI simplifies the complex and critical borrowing process, identify the potential risks in giving the loan by analyzing market trends, prospect’s behavior, and identifies even the slightest probability of fraud.
Risk Management And Fraud Detection
The Punjab National Bank scam exposed the banking sector to an enormous amount of risk and shook the regulators, financial and stock markets, and the banking industry. AI and due diligence can monitor such potential threats and help banks install fool-proof surveillance and fraud detection systems. Surveillance in banks has been through audits and sampling. Some data sets and files that are capable of causing huge risks may not be covered in these samples. The algorithmic rules-based approach can help in monitoring of each and every file, and machine learning techniques can keep a database of all such files which pose a risk to the bank.
Banks, while providing secure and swift transactions, can use AI to detect the fraud in the transactions or find out any suspicious activity in the customer’s account on the basis of behavior analysis. With an increasing percentage of cybercrimes in recent years, AI services can be used to maintain cyber-security and most importantly, in safeguarding personal data. Citibank has invested over $11 million in a new anti-money laundering structure already using machine learning and big data.
AI-based systems can help in compliance by judging the functionality of the internal control systems. AI solutions can also be a game-changer by detecting insider trading that leads to market abuse.
Insurance underwriting and claims:
In this era of bancassurance, customers are more likely to come to banks rather than visit insurance agencies. The insurance sector can reap the benefits of AI in underwriting, claim-handling procedures, and fraud detection. It can also help in identifying risky behavior and charge higher premiums to those groups of customers. Insurance firms have an enormous amount of data that can help make mathematical models and predict risky behaviors accurately. Such data can also be lent to banks to be used in customer risk identification. This reduces the turn-around-time (TAT) for both loans and insurance. For example, to analyze the damage to a vehicle, deep learning techniques can analyze an image of the vehicle, and calculate repair cost using predictive models.
Threats Posed By AI
Jack Ma, the founder of Alibaba, warned the audience at the World Economic Forum 2018 at Davos, that AI and big data were a threat to humans and would disable people instead of empowering them. A massive deployment of AI in banks would come with its share of risks and opportunities. Banks increase their investment in AI every year, often at the risk of becoming obsolete. But what we also need to understand is the risks to the system that AI can pose.
1. Loss Of Jobs
Banks face the risk of backlash from their employees due to the potential automation of tasks, which can lead to job loss and job reassignments. AI, in the garb of increasing enterprise productivity, will reshape the way the employees perform their jobs. This could lead to possible dissatisfaction among employees, resulting in resignations or employees being fired due to inefficiency. AI can replace a teller, customer service executive, loan processing officer, compliance officer, and even finance managers.
2. The Opacity Of Processes:
While deep learning models and neural networks in AI have proven over time to be perfect than human decision-making, they are often not transparent in terms of revealing how they generated such conclusions. It then becomes a challenge for bankers to explain that to the regulators. Justice Srikrishna Committee has mentioned that the biggest challenge in using big data, artificial intelligence is that they operate outside the framework of traditional privacy principles. This could now act in a reverse way and expose banks to risks without their knowledge. It could also possibly give rise to hidden biases in decision making since AI has access to data of all the customers.
3. Reduced Customer Loyalty
There is also a fear of reduced customer loyalty due to less customer contact and the lack of essence of “human touch.” Banks, especially in India, have an emotional value as they help many in cherishing their long-standing dreams — be it a beautiful house or a good education for students. All this could be lost due to AI and automation. The socio-economically backward groups would be the biggest losers and most affected in such a scenario due to low levels of education and the digital divide.
The Way Forward
Nick Bilton, a tech columnist, wrote in the New York Times, “The upheavals [of artificial intelligence] can escalate quickly and become scarier and even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer, could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to the disease.” The message conveyed here is that banks have to develop an understanding of the effects of digitization and develop an expansive foresight into the prospects of AI — so that we as humans have control over AI and not the reverse. The area that banks should now focus on is Data Acquisition. The lack of proper customer records is the biggest hindrance to AI.
We should ensure that the data used by banks are KYC compliant clean data as these would be used in AI models. Massive data infrastructure is required to leverage AI. Proper inspection of data and checks of accuracy are also needed before using such technologies in the public domain.
Analysis and standardization of data:
The amount of data with banks is so enormous that Oracle and Accenture have entire departments storing bank data. What we need is a proper analysis of the data, and that requires a high level of leadership skill to bring together cross-functional teams — one with knowledge of financial business, and the other with requisite machine learning skills to formulate a plan and infrastructure across various departments for efficient usage of such data sets. AI remains a niche-oriented domain with a shortage of talent and expertise.
Leakage And Misuse Of Data
Several experts in the U.S. and the U.K. opine that cyber, political, and physical threats arise with the growth in the capabilities and reach of AI. The recent Facebook scandal highlights the risk corrupt data practices can bring to a firm. Complete transparency while venturing into new AI projects also should be ensured so that banks don’t face reputation risks.
Banks should start building AI systems with a small set of complex data and add subsequent ones, thus creating a universal record of each client. Adequate investments should be done on the safe storage of data and prevent it from leakage. This will help the bank detect potential hazards in the implementation stage of the project and enable efficient identification — and then execution — of goals and priorities of the organization. Artificial intelligence will soon become the sole determinant of the competitive position of banks and a key element enhancing their competitive advantage.
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