Table of Contents
Introduction
For the last few years, fraud has threatened to become the most pressing issue in banking activity since cybercriminals are constantly inventing new forms of activity. A cybercriminal can pilfer others’ identities and use those details to carry out unlawful operations, and financial institutions have a daunting task of tackling fraudulent activities.
This is where the application of artificial intelligence in the form of chatbots in banking has been rising to fight fraud in real time. Unlike the conventional detection of fraud systems, AI-powered chatbots incorporate machine learning, NLP, and predictive analytics for customers’ behavior, indicate potential transaction risk, and cause an immediate alert to those concerned. It is possible to state that the increased use of AI chatbots in the banking sector contributes to the improvement of the security system of banks and increases customers’ trust.
According to RBI data ₹3,207 crore was lost because of 5,82,000 cases of cyber fraud between FY2020 and FY2024 in India.
In this blog post, we discuss the Concrete and perceived roles of AI chatbots in banking, with a special highlight on real-time fraud detection and prevention. We also mention the distinctive benefits to be yielded and the strategic ways financial institutions can incorporate this instrument.
What are AI chatbots in banking?
These are AI-powered conversational agents designed to automate customer interactions, improve safety, and simplify financial services. The AI-driven customer interactions, which are powered by the chatbots, utilize Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) to understand customer queries, process transactions, and even detect fraudulent activities in real-time.
AI-driven banking chatbots are different from old-style chatbots that depend on pre-set rules and can learn from customer interactions, find patterns, and give personalized and real-time replies. Banks have them embedded in their platforms such as mobile applications, websites, WhatsApp, and even voice assistants all of which enable customer support 24/7.
What are the challenges in the banking industry?
Financial institutions have to adapt to new technologies quickly, but at the same time, they are experiencing several problems that affect their productivity, security, and the trust of their customers. Below are some of the key issues facing the banking sector:
Fraud and cybersecurity threats
There has been a surge in phishing attacks, identity theft, and financial fraud. Banks are often targeted by cyberattacks, so real-time fraud detection and prevention is essential to combat financial crime.
Regulatory compliance
Financial institutions must provide the constantly changing financial regulations and compliance requirements (e.g., KYC). There is a considerable risk of heavy fines and reputational damage due to non-compliance. Regulatory solutions should be automated to minimize manual workload and save time for dedicated compliance officers.
Pressure for digital transformation
The digital transformation of banks has been slow and has resulted in banks still relying on outdated servers, software, and processes. However, there are challenges in migrating data from old to new systems, which often puts a burden on the banks.
Challenges of fintech companies
There has been a rise in digitalization in the financial sector and the rise of fintech companies. It has allowed customers to engage in mobile payments through fintech apps and internet banking, disrupting traditional banking.
However, traditional banks have been slow to adapt to the changing needs of their customers.
Uncertain market conditions
Managing risk through market conditions with rising inflation and changing interest rates has been damaging to the stable revenue generation of the banks. There has been increasing evidence of users defaulting on their credits, leading to heavy losses.
Growing customer expectations
Customer expectations have been growing for convenient banking every time. There has been growing demand for 24/7 banking facilities with customization of products and services. Additionally, customers wish to access banking services through various digital platforms like SMS, chat, web, or mobile. Banks are struggling to meet customer demands for complete digitalization of their services.
How AI chatbots in banking detect fraud in real-time
Chatbots in banking use various technologies to identify suspicious activities in real time. They use natural language processing (NLP), machine learning (ML), deep learning, and large language models (LLM) to monitor transactions and analyze user behavior. Here are a few they detect fraud using such technologies;
Multi-factor authentication
Chatbots in banking software often implement multiple steps for authenticating a user. Chatbots may require OTPs (one-time passcodes), biometric verification, or pre-set security questions to allow access to financial accounts.
Often, when we log in from a different device, chatbots verify if the registered user is accessing the account or if it’s unauthorized access in the absence of the user. They confirm by sending a notification to the original registered device with the account.
Monitoring transactions
Chatbots can monitor transactions round-the-clock. They quickly raise a flag if there is an unusually large transaction, multiple login attempts, multiple OTP generations, or other device logins. If fraudulent activity meets certain predefined conditions, they can instantly alert users to verify their account integrity and encourage them to change passwords.
User behavior monitoring
Chatbots also engage to learn and analyze user behavior 24/7. They may monitor for number of login attempts, withdrawal amounts, transaction history, etc. These data are used to draw patterns, which may indicate chances of fraudulent activity. If conditions meet certain risk parameters, they can alert users or inform banking institutions for further action.
Identifying phishing activity
Receiving phishing links through emails, SMS, or chat has become quite common. Vigilance is the key, and chatbots keep a continuous eye on such activities. They use their natural language processing (NLP) and large language model (LLM) capabilities to scan through emails, messages, and chat to detect phishing links. If detected, they can raise the flag to the user with warnings of suspicious content.
Risk scoring
Chatbots use machine learning to analyze vast data to identify and allocate a risk score on transactions based on their history and behavior related to the account. Any risk score above an assigned threshold level is automatically raised for verification.
Suspend account on fraud detection
AI chatbots can quickly analyze transaction patterns and subtle user behavior. If any transaction is above and beyond the threshold of risk score, they immediately freeze the account or halt further transactions to thwart further unauthorized access. Users are notified of their account freeze, and upon further verification and security updates, they can get their account reactivated for further use.
What are the benefits of chatbots in banking in fraud prevention?
Chatbots in banking present multi-faced benefits, but their contribution to fraud prevention is tremendous. It will allow banks to handle customers’ money with safety and build trust. Here are some ways that chatbots can prevent fraudulent activities in banking;
Continuous analysis and notification
Digital functionality allows chatbots to be active 24/7, unlike human banking agents who are unavailable after their working hours. Chatbots can monitor digital transactions at any time of the day. Even a single chatbot can monitor vast amounts of data related to a financial institution in real time.
Notification of suspicious transactions
If any transaction raises suspicion due to its pattern compared to its records and user behavior, a chatbot can immediately notify users through SMS, email, or in-app messages.
User authentication
When faced with a suspicious transaction, a chatbot can engage with the user to authenticate their identity in real-time. To verify identity chatbots can use OTPs, biometric data, or registered devices to ensure authorized personnel can only access the account when security seems to be compromised.
Behavioral analysis
Chatbots actively analyze user behaviors to understand transaction patterns through habits, spend limits, number of transactions, device logins, etc. It allows chatbots to identify if an authorized user is using the account.
Simple reporting
Chatbots can present a simplified report to highlight suspicious activities with the necessary parameters. For common users, it can summarize the report data for users to deduce logical information and take vigilant action to safeguard their accounts against threats.
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The future of chatbots in banking security
Today, with increasing digitalization in financial institutions, the threat of fraudulent activities has not just increased in number but has evolved in ways. However, advancements in AI technologies offer a promising advantage to banks in securing user accounts, analyzing default risk, and quickly safeguarding compromised bank accounts.
Here are some advantages that chatbots offer for ramping up the safety of digital transactions;
Predictive analysis
Since chatbots can analyze vast amounts of data, it gives them leverage to predict potential threats in advance. Using machine learning, they can analyze historical transaction patterns and identify any anomaly or unusual pattern to raise notification for chances of fraud.
Advanced biometric authentication
Though there have been advancements in biometric verification of users, most banks still rely on passwords and PINs to secure accounts. However, as fraudulent activities are increasing and advancements in AI technologies are made, we may observe the integration of voice or facial recognition with chatbots in banking for authentication to access accounts.
Automated threat response
Deep integration of AI technologies can ensure automated action to threats in the form of account deactivation, mandate multi-level authentication, or even categorize suspicious accounts to notify authorities.
Blockchain integration
Developments to strengthen banking transactions from evolving cybersecurity threats will allow the use of blockchain technology. The decentralized ledger based on blockchain reduces the chances of tampering and fraud by increasing transparency in handling account transactions.
While the unalterable records allow transaction authenticity, its advanced cryptographic technique secures accounts using hashes and digital signatures in a pre-defined pattern, which allows for better protection of user data.
Risk-based authentication
AI chatbots often use risk-based scores to identify suspicious transactions. Chatbots can be extensively used to secure transactions. For small low-risk payments, it may require minimal authentication, while for high-value transactions, international payments, transfers of amounts to suspicious accounts may require higher authentication levels.
Conclusion
Chatbots in banking are an essential tool not only for improving customer experience through streamlined service and support, but they are integral against cybersecurity and fraud.
Their advanced machine-learning capabilities and natural language processing allow them to monitor vast amounts of transactions and quickly raise threat alerts with the highest accuracy.
As AI technologies are on the ride for continuous advancements, we can expect extensive applications of chatbots in banking for active risk mitigation and identification of fraudulent activities with much more precision and speed.
Frequently Asked Questions
Chatbots in banking can identify a range of fraud activities such as;
• User impersonation
• Account hack
• Unauthorized transactions
• Phishing scams
Chatbots in banking can identify suspicious activities in real-time. Once a threat or fraud activity is detected, it can notify the user immediately and freeze the account to thwart further losses.
Chatbots in banking use natural language processing to engage with users in near-human-like conversations. During conversations, they can direct questions to the users to authenticate their identity, verify transactions, or if any unauthorized transaction has happened, then it can guide the user in accurately reporting the incident by asking relevant questions.
There are numerous ways that banks can use chatbots in their operations, such as;
• Automation of account-related user requests
• Assist users in FAQs
• Personalization of services
• Customer support
• Detect and notify fraudulent activities
• Collect reviews & feedback
AI in banking uses natural language processing and machine learning to analyze unusual transactions compared to historical data, monitor login attempts, and detect authentication failures to identify suspicious activities. Using large language models, it can scan the contents of the email, SMS, or chat to identify phishing links and notify the user in real time.
Chatbots in banking hold immense potential in various processes across multiple departments, such as;
• Customer support
• Personalization of services
• Cybersecurity
• Lead generation
• Upselling or cross-selling
Banking institutions have been proactive in leveraging the benefits of AI in improving operational efficiency, engaging users with personalized conversations, protecting against cyberattacks, personalizing services, and identifying credit risk of users among various other potentials.
Banking chatbots use natural language processing, machine learning, intent recognition, and sentiment analysis to comprehend and respond naturally to users. Using customer data, they personalize conversations and resolutions, ensuring users are satisfied. Chatbots are quick and present 24/7 to assist users with their queries.
Sentiment analysis allows them to detect user emotion and refine responses with empathetic dialogue to connect and assist users. These are some of the capabilities of advanced banking chatbots that have helped improve customer service by enhancing customer experience.
Nisha Sneha
Nisha Sneha is a passionate content writer with 5 years of experience creating impactful content for SAAS products, new-age technologies, and software applications. Currently, she is contributing to Kenyt.AI by crafting engaging content for its readers. Creating captivating content that provides accurate information about the latest advancements in science and technology has been at the core of her creativity.
In addition to writing, she enjoys gardening, reading, and swimming as hobbies.
