Explain how can AI be used in detecting fraud?

Answer:

Using AI to detect fraud has aided businesses in improving internal security and simplifying corporate operations. Artificial Intelligence has therefore emerged as a significant tool for avoiding financial crimes due to its increased efficiency.

AI can be used to analyze huge numbers of transactions in order to uncover fraud trends, which can subsequently be used to detect fraud in real-time.

When fraud is suspected, AI models may be used to reject transactions altogether or flag them for further investigation, as well as rate the likelihood of fraud, allowing investigators to focus their efforts on the most promising instances.

The AI model can also offer cause codes for the transaction being flagged. These reason codes direct the investigator as to where they should seek to find the faults and aid to speed up the investigation.

AI may also learn from investigators when they evaluate and clear questionable transactions, reinforcing the AI model’s knowledge and avoiding trends that don’t lead to fraud.

Machine learning is a term that describes analytic approaches that “learn” patterns in datasets without the assistance of a human analyst.

AI is a wide term that refers to the use of particular types of analytics to complete tasks ranging from driving a car to, yep, detecting a fraudulent transaction.

Consider machine learning to be a method of creating analytic models, and AI to be the application of those models.

Because the approaches enable the automatic finding of patterns across huge quantities of streaming transactions, they are very successful in fraud prevention and detection

Q2. Describe different Approaches To knowledge representation.

ANSWER:

There are 4 main approaches to knowledge representation in artificial intelligence, viz. simple relational knowledge, inheritable knowledge, inferential knowledge, and procedural knowledge.

• Simple Relational Knowledge

This is a relational method of storing facts which is among the simplest of the method. This method helps in storing facts where each fact regarding an object is providing in columns. This approach is prevalent in DBMS (database management systems).

• Inheritable Knowledge

Knowledge here is stored hierarchically. A well-structured hierarchy of classes is formed where data is stored, which provides the opportunity for inference. Here we can apply inheritance property, allowing us to have inheritable knowledge. This way, the relations between instance and class (aka instance relation) can be identified. Unlike Simple Relations, here, the objects are represented as nodes.

• Inferential Knowledge

In this method, logics are used. Being a very formal approach, facts can be retrieved with a high level of accuracy.

• Procedural Knowledge

This method uses programs and codes that use simple if-then rules. This is the way many programming languages such as LIST, Prolog save information. We may not use this method to represent all forms of knowledge, but domain-specific knowledge can very efficiently be stored in this manner.

Q2.  How is Computer Vision and AI related?

ANSWER:

Computer vision is a field of AI that trains computers to capture and interpret information from image and video data. By applying machine learning (ML) models to images, computers can classify objects and respond—like unlocking your smartphone when it recognizes your face.
Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information.
AI-based computer vision can sense the surroundings to identify various objects, such as pedestrians, traffic signals, and more, on the road. Moreover, it also helps in measuring the distance of the vehicle from other vehicles. The technology helps a device to recognize the face to verify the identity of the person.