How Artificial Intelligence is revolutionizing the banking sector
Banks and Financial Institutions are beginning to recognise the significance of Artificial Intelligence (AI) to solve real business issues. In a recent EY survey of senior executives, 85% of respondents revealed that they have deployed AI in their banking operations and expect to use AI in the coming years for new use cases. Additionally, 64% of respondents are willing to use AI for process automation, new revenue generation, customer service and client acquisitions and risk management within the next two years.
Banks are leveraging the power of AI to enhance customer-centric dialogue—taking a leap from basic transactions to engaging interactions. Let us see the top 5 use cases across banking where AI is making a positive impact.
Regulatory Compliance and Risk Management – AI can enable banks to deploy end-to-end risk management and regulatory compliance. With the help of strategic data acquisition and data assessment, AI tools can screen for cases, risks and mandates to guarantee compliance control. AI can also study historical trend data to predict regulation overlaps and foster more accurate and timely compliance. Internally, AI can be used to align policies and assess the adequacy of business operations like employee training.
Anti-Money Laundering (AML) – Transaction monitoring has become increasingly challenging for banks as the volume of online transactions grow. Banks have to actively monitor high levels of money laundering threats and separate meaningful risk alerts from spurious data. AI adoption helps banks move from rule-based analysis to risk-based assessments. This helps in streamlining compliance processes and flagging off false positives or fraudulent transactions.
Customer Onboarding – Customer onboarding is a complex and tedious process for banks as it is document-hungry and involves different levels of verification. AI facilitates OCR driven data capture, eKYC, facial recognition, compliance check, digital signature and real-time coalition of customer data. On the other hand, it also reduces operational costs for the banks and accelerates the speed of customer onboarding.
Data collection and analysis – Most data reserves for banks are either in silos or fragmented across multiple use cases. With AI capability, banks can establish a centralized data centre to analyze relevant data and enable data security. AI can also collate huge customer data sets to generate detailed reports on their financial activities. This helps in reducing redundant or repetitive tasks and improves the bank’s decision making when dealing with customers. Additionally, for mobile banking apps that regularly collect user data, AI can be leveraged to provide customers with distinctive omnichannel experiences that are personalized to improve customer engagement.
Servicing queries – With AI, banks have ushered in the era of conversational banking by deploying chatbots and virtual assistants. Chatbots have the ability to generate instant responses for customer queries promoting engaging conversations and customer reliability. Another advantage is that virtual assistance can be accessed by customers 24/7 and solves customer issues in real-time. Moreover, chatbots gain insights from large troves of customer data to proactively deliver suggestions to drive superior customer satisfaction. New additions like Interactive Voice Response (IVR) allows customers to converse with chatbots to carry out simple tasks such as checking their balance or getting information on recent transactions.
With the above-mentioned examples, we can conclude that AI disruption is redefining banking as we know. As per McKinsey, the application of AI technologies can help banks and financial institutions to generate more than $250 billion in value especially to enable fraud prevention and overall risk management. With a wide range of applications, AI has the ability to offer higher complexity solutions that positively impact customer experience and drive significant cost savings for the bank.
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