Artificial Intelligence (AI) in Banking 2021 – Datamation

Artificial intelligence (AI) is one of the most disruptive technologies today, and the banking industry is a prime example of its potential.
AI in banking is a relatively new concept, but it’s already making banks safer, more efficient, and accessible.
The financial services sector represents 20-25% of the global economy and must continuously evolve to meet consumers’ rising needs. Banks must be highly efficient and secure, which is increasingly difficult amid cybercrime and growing user demands. AI can provide solutions.
AI in banking already showcases impressive adoption figures.
According to one survey, 75% of banks with $100 billion or more in assets implement AI, and 46% of smaller banks do. Overall, 80% said they’re at least aware of the potential benefits of implementing AI strategies.
According to McKinsey, AI could deliver $1 trillion of additional value to the banking industry each year. The potential benefits are so significant, because AI can improve processes in virtually every aspect of banking. From back-office automation to customer-facing operations, AI is delivering considerable advantages to the banks implementing it.
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One of the most significant applications of AI in banking is machine learning (ML) fraud detection.
As technology has made banking more accessible, it’s also opened the door for increased fraud to take advantage of these services. Machine learning, which is typically faster and more accurate than humans at connecting data points, is a way to identify fraud.
Danske Bank, the largest bank in Denmark, implemented a fraud detection algorithm after discovering its old rule-based system was insufficient. The deep learning tool proved 50% more accurate at detecting fraud and reduced false positives by 60%.
Since these systems become more accurate with larger data sets, they become better the longer banks use them. As more institutions implement AI fraud detection, fraud should become less successful.
AI can help banks discover and manage cyber threats.
In 2019, the financial sector accounted for 29% of all cyber attacks, making it the most-targeted industry. As these attacks rise in frequency and severity, human IT workers may not be able to spot or stop them all, raising the need for AI cybersecurity.
JPMorgan Chase developed an AI algorithm in 2019 to detect malware, Trojans, and phishing threats. The system can find these issues “even ahead of the actual spear-phishing campaign,” according to the researchers behind it. 
With tools like this, banks can respond to potential cyber attacks before they affect employees, customers, or internal systems. Without the continuous monitoring capabilities of AI, this level of vigilance would be near impossible.
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Offering loans and credit services are inherently risky.
Banks must determine how much risk any given scenario presents, but factors like credit scores aren’t always reliable metrics. AI can improve these processes by analyzing a broader range of factors to make more informed decisions.
As the Bank Policy Institute points out, conventional credit underwriting systems “serve less well other creditworthy consumers who are unbanked or under-banked.” AI, on the other hand, can analyze more data from more diverse sources in less time. It can then offer a better picture of someone’s creditworthiness, without the biases and limits of traditional systems.
A study of institutions using AI credit underwriting highlighted how they expand loan accessibility. AI models approved 27% more applicants and yielded 16% lower interest rates.
Another leading banking concern that AI can address is regulatory compliance.
As banks implement new technologies and services, they have to ensure they meet relevant standards. This can be challenging with current innovation rates and changing regulatory landscapes.
Ravi Gedela, CEO of AI finance intelligence company Banking Labs, explains how traditional compliance programs can be overly complicated: “A regulatory compliance program requires multiple lines of business and corporate functions to come together … but this often leads to questionable quality, a mismatch of formats and standards, and confusion.”
AI can make sense of these disparate data sources and consolidate them into understandable insights. Banks can then improve their compliance amid increasingly complex regulation.
AI in banking can help automate and improve customer-facing operations.
Chatbots, facial recognition, and natural language processing (NLP) make banking apps easier to use, improving customer satisfaction. As fintech services threaten to disrupt the finance industry, these advantages become crucial for banks.
Bank of America introduced a chatbot named Erica in 2018 to impressive results. By 2019, Erica had served 10 million users, processing 100 million customer requests. The chatbot can answer financial questions, help customers make payments, and assist more users without needing more staff.
The 24/7 availability of chatbots lets banks serve their clients at any time despite potential worker shortages. This improved accessibility helps banks provide higher customer satisfaction and generate loyalty.
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