ABSTRACT: –

Internet banking has their separate account for  customers and is controlled through Banks or retail stores. The purpose of this paper is to hit upon and save you from fraud in case of net banking the usage of Hidden Markov Model algorithm. At the same time, we have attempted to make sure that authentic transactions aren’t rejected through using one time password that changed into generated through the Bank server and despatched to the specific clients via SMS to their Mobile variety that is registered withinside the system. Banks are in search of to reduce massive losses via fraud detection and prevention systems. Many distinct superior fraud technologies are being carried out for fraudulent Internet banking transactions detection and prevention. However, they don’t have any effective detection mechanism to perceive valid customers and hint their illegal activities. We suggest a version to conquer these problems: the usage of the Hidden Markov Model.

SYSTEM:-

• data collection: collect a dataset of internet banking transactions that includes both legitimate and fraudulent transactions this dataset should be representative of the types of transactions that occur in real-world scenarios
• data preprocessing: involves preprocessing the collected data to ensure that it is clean and suitable for analysis this includes cleaning the data removing duplicates and performing any necessary data transformations .
• model training: train an hmm model on the preprocessed data the model should be trained on both legitimate and fraudulent transactions to enable it to distinguish between them.
• model testing: test the trained model using a separate set of data this data should include both legitimate and fraudulent transactions the model should be evaluated based on its ability to correctly classify these transactions.
• fraud detection: uses the trained model to detect fraudulent transactions in real-time this involves monitoring incoming transactions and comparing them to the models predictions if a transaction is classified as fraudulent appropriate action should be taken such as flagging the transaction for review or blocking the transaction.
• model refinement: continuously refine the model based on new data and feedback this involves retraining the model on new data and tweaking its parameters to improve its accuracy and effectiveness.

PROPOSED SYSTEM:-

• We will expand a dummy financial institution account database

For numerous cutomers.

• A net provider primarily based totally on Tomcat Apache server will be created and deployed to permit customers to make use of on-line banking.
• We may even expand a few consumer packages in order to permit the person to make on-line bills for required services. E.g. Buy Air Tickets, Buy Train Tickets, Movie Tickets, Transfer Amount, Shop Online, etc.
• Based on records of banking transactions we are able to layout the database in order to store purchaser transaction patterns.
• Using this sample we are able to layout an evaluation algorithm primarily based totally on HMM in order to compare if the on-going transaction is fraudulent or original.
• We shall use Servlets for consumer-server communication.
• We may even layout a consumer aspect utility and a server aspect utility the usage of Java A WT / Swing.
• The consumer utility shall speak with server the usage of Java Networking.
• The consumer utility will use Serialized Objects for transacting with server.
• To put in force the HMM set of rules we will make use of Java Collections API.
• The database can also be maintained the use of Serialized Objects. On fmding a fraudulent transaction we will ship the One time password to the consumer to a definitely affirm the identification of consumer and retain with transaction in case the real consumer is definitely starting up it.

MODULES:-

• Data Collection Module: This module collects data from various sources such as bank transactions, credit card transactions, etc. It stores the data in a suitable format for further processing.

• Data Preprocessing Module: This module performs cleaning, normalization, and transformation of the collected data. It also removes any duplicates or irrelevant data and ensures the data is suitable for HMM analysis.

• Feature Extraction Module: This module extracts relevant features from the preprocessed data that will be used for HMM model training. Examples of features that can be extracted include transaction amount, location, time, type of transaction, etc.

• HMM Model Training Module: This module trains the HMM model on the extracted features from both legitimate and fraudulent transactions. The model learns to differentiate between the two by analyzing the hidden states and output probabilities.

• Model Evaluation Module: This module evaluates the trained model’s performance by testing it on a separate dataset that includes both legitimate and fraudulent transactions. The model’s accuracy, precision, and recall are evaluated to assess its performance.

• Fraud Detection Module: This module uses the trained HMM model to detect fraudulent transactions in real-time. It receives incoming transaction data, applies the model to the extracted features, and classifies the transaction as legitimate or fraudulent.

• Alert Generation Module: This module generates an alert when a transaction is classified as fraudulent. The alert can be in the form of an email, SMS, or push notification. The alert includes details of the fraudulent transaction, allowing the bank to take appropriate action.

• Model Refinement Module: This module continuously refines the HMM model using new data and feedback. It re-trains the model on the new data and adjusts its parameters to improve its accuracy and effectiveness.

Overall, these modules work together to form a comprehensive Internet Banking Fraud Detection System using HMM.

APPLICATION:-

• real-time fraud detection: The system can be used to detect fraudulent transactions in real-time. allowing banks to take immediate action to prevent financial losses.
• improved customer trust: the system can increase customer trust by ensuring that their accounts are protected from fraudulent activity. This can lead to increased customer loyalty and satisfaction
• cost savings: the system can save banks money by reducing the costs associated with fraud detection and prevention. by detecting and preventing fraudulent activity early banks can avoid expensive chargebacks and legal fees.
• Compliance:the system can help banks comply with regulatory requirements related to fraud prevention by implementing a robust fraud detection system. Banks can demonstrate to regulators that they are taking the necessary steps to protect their customers through an early warning system the system can act as an.
• early warning system: for potential fraud trends by analyzing patterns and trends in transaction data the system can alert banks to potential new types of fraud and allow them to take preemptive measures to prevent them.
• customer analytics: the system can be used to analyze customer behavior and identify patterns that may indicate fraudulent activity this can help banks better understand their customers and develop targeted fraud prevention strategies.

HARDWARE AND SOFTWARE REQUIREMENTS:-

HARDWARE:-

• Processor: Intel Core i3 or more.
• RAM: 4GB or more.
• Hard disk: 250 GB or more.

SOFTWARE:-

• Operating System : Windows 10, 7, 8.
• Python
• Anaconda
• Spyder, Jupyter notebook, Flask.
•