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Knowledge Nugget: MuleHunter.AI
subject: Technology and Economy
(Relevance: Artificial Intelligence (AI) and its applications in various fields have become essential in our lives. With increasing incidences of digital fraud, it is important to understand how AI-powered models can help address this issue. Additionally, UPSC has previously asked questions related to AI and its applications. Hence, knowing about MuleHunter.AI is important for your UPSC preparation.)
Why in the news?
On Friday (December 6), the Reserve Bank of India (RBI) announced that it has developed an AI-powered model. MuleHunter.AIThat could reduce digital fraud by helping banks deal with the growing problem of “mule” bank accounts. It has been developed by Reserve Bank Innovation Hub.
Key takeaways:
1. MuleHunter.AI enables detection of Mule Bank Accounts in an efficient manner. An RBI statement said the pilot with two large public sector banks has yielded encouraging results and asked the banks to collaborate with RBIH to develop the MuleHunter.AI model “to address the issue of mule bank accounts being used for financial fraud.”
2. The government is also taking steps to crack down on mule accounts. On Friday, the Financial Services Department Secretary held a meeting with the RBI, the Indian Cyber Crime Coordination Center (I4C), which is affiliated to the Union Home Ministry, the National Bank for Agriculture and Rural Development, and public-private officials. -Sector banks to discuss the growing challenge of digital financial fraud, especially mule accounts.
3. In Friday’s meeting, banks were urged to leverage best practices, state-of-the-art tools and promote inter-bank collaboration to effectively address mule accounts. They were also encouraged to explore and implement RBI’s Mulehunter.AI solution to detect and monitor mule accounts.
Mule Bank Account
1. Mule bank accounts are seen as a key element in most online financial frauds in India. notably, The Indian Express reported last month That the Center has frozen around 4.5 lakh such bank accounts, which were commonly used for laundering cyber crime money.
2. A mule account is a bank account used by criminals for illegal activities, including laundering of illegal funds. A mule account is usually bought by criminals from their original users, often people from low income groups, or people with low levels of technical literacy.
3. The word “money mule” Used to describe innocent victims that criminals use to steal or illicit money through their bank accounts. When such cases are reported, money mules can become the target of police investigations, because it is attached to their accounts, while the real criminals cannot be traced.
BEYOND THE NUGGET: Artificial Intelligence, Machine Learning and Deep Learning
1. Artificial Intelligence (AI) is the ability of machines, especially computers, to perform tasks that normally require human intelligence. These tasks include things like understanding language, recognizing patterns, solving problems, and making decisions.
2. Breakthroughs in computational power and big data accelerated AI’s capabilities in image and speech recognition, natural language processing, and autonomous systems. Today, AI continues to evolve, integrating across industries, driving innovation, and changing everyday life.
3. AI can be classified into two types: Artificial Narrow Intelligence (ANI) also known as weak AI and Artificial General Intelligence (AGI) also known as strong AI.
4. ANI is designed for specific tasks and excels within a narrow domain. Examples include virtual assistants like Siri, recommendation systems on platforms like Netflix, and image recognition software.
5. AGI aims to replicate human cognitive abilities, enabling it to perform any intelligent task a human can perform. AGI will have general reasoning skills, understand context, and adapt to new situations in different domains. It will be able to solve problems without the need for autonomous learning and task-specific programming.
6. Machine Learning (ML) and Deep Learning (DL) are subsets of AI but differ in complexity and capabilities.
7. Machine learning It involves training algorithms to learn and make predictions from data and often requires manual feature extraction.
8. deep learning is a subset of ML and uses neural networks with multiple layers (hence “deep”) to automatically learn features from large datasets. While ML works well with small datasets, DL requires large amounts of data and computational power.
(S0urces: How RBI is leveraging AI to crack ‘mule bank accounts’, What is artificial intelligence, How it is revolutionizing healthcare)
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