Establishing Security With ML Based Fraud Detection For Enterprises
Posted By : Arpita Pal | 28-Oct-2023
The advent of digital transformation in financial systems has changed the course of how we deal with financial transactions at both individual and enterprise levels. Portable facilities such as net banking, debit and credit cards, digital wallets etc have negated the requirement of physical presence of the users to carry out financial transactions and related functionalities. However, with advancements in technologies, there has also been an unprecedented rise in fraudulent activities that have been posing a major threat to peopleâ€™s life earnings and crucial financial resources for organizations. Some of these fraudulent activities affecting financial transactions include counterfeit frauds, identity theft, and payment-related frauds through credit/ debit cards, mobile wallets, net banking etc. To eliminate such exploitation of financial systems and innocent people, progressive technologies such as artificial intelligence and machine learning are being utilized for timely identification and blocking of these activities.
Machine learning has proved to be extremely beneficial in combating real-time challenges in multiple fields as it is able to provide high precision and speed in resolving issues and get better with time with its self-learning abilities. By utilizing ML based fraud detection, institutions can combat fraudsters that take advantage of vulnerabilities caused by a lack of resources or expertise in payment processing. This article aims to explore how machine learning for fraud detection can serve as an effective measure in countering illegal activities and the approaches it employs for providing optimum security to its users.
Models Employed in Machine Learning for Fraud Detection
Supervised Learning: Popular for solving problems such as Regression and Classification, it detects relationships and patterns between the input data and output labels under dedicated supervision. These labels describe features of the data that help the algorithm to differentiate between specific data from irrelevant data. This aids in predicting accurate labels when presented with unfamiliar data. Similarly for fraud detection, algorithms in machine learning are trained on labeled data sets, which are able to classify data into valid and fraudulent categories. Other than fraud detection, it is also useful for image classification, anomaly detection risk assessment etc.
Unsupervised Learning: In unsupervised model of machine learning, the computer algorithm is not trained under specific supervision, hence its name. It detects similarities, patterns and relationships from unstructured data without any guidance on labeled outputs. The two main algorithms employed in unsupervised learning are clustering and association which attempt to detect groupings and rules determining specific associations in data. Unsupervised learning offers more flexibility and cost efficiency as compared to supervised learning in machine learning.
Semi-Supervised Learning: By adopting traits from both supervised and unsupervised learning, semi-supervised learning consists of a computer algorithm trained on a few specifically labeled outputs that work on extensive unstructured data to find similarities, patterns and relationships. It is utilized for solving multiple use cases in regression, clustering, classification and association problems. Some examples of semi-supervised learning include speech recognition, text document and web content classification, etc. However, it is not advised to use semi-supervised learning in cases where there training labeled data is not representative of the entire data as it will not be able to produce results with higher accuracy.
Reinforcement Learning: Unlike supervised and unsupervised learning where the aim is to find patterns and relationships within data, reinforcement learning focuses on collecting higher rewards through the completion of certain tasks through pathways that offer maximum rewards in a situation. Reinforcement learning constantly improves itself through trial and error to find the best action pathway that can produce the highest amount of rewards. It works sequentially where input and output data are dependent on each other, hence making the final decision dependent on it as well.
Reinforcement learning consists of positive and negative reinforcement learning that involves rewards and punishments to encourage or discourage certain actions within the process. As it is able to solve highly complex problems of abstract type, its application is found in robotics, video games and real-life situations where it can find the most optimum process of finding a solution according to a specific situation.
Process Involved In Setting Up Machine Learning For Fraud Detection
A) Input of Data: The first step involves feeding the algorithm with data that is used for training the model. The quality of data is essential as it will be the governing force for the modelâ€™s accuracy and overall performance in fraud detection. Also, the more amount of data is fed into the model, the better it will get at recognizing similarities, patterns and relationships between authentic and fraudulent data. With changing technologies and new illegal scams popping up with time, experts will have to ensure that the model is trained with relevant data for best performance in fraud detection.
B) Features Extraction: After the input of data, it will have to be tailored for the algorithm that will facilitate easier adoption so that it can perform accordingly as per its dedicated objective. The data scientists behind the model will modify the raw data including vast amounts of financial information into elements easily understandable for it on the basis of which it will perform functions like identifying similarities, outliers, patterns and relationships for filtering out data indicating fraudulent activities. The data can include information such as customer identity, payment methods, location, etc that will help the machine learning algorithm in distinguishing and accurately identifying various elements of the input data for better fraud detection.
C) Training The Algorithm: Machine learning algorithms are governed by instructions and rules that prescribe the set of functions it will perform and the objectives it is meant to achieve. After feeding and extracting features from the data to the algorithm, it will be trained to perform functions that contribute to the objective of achieving fraud detection. The algorithm will be trained on previous customer data to better understand the elements of information involved. With repeated training, the algorithm will be able to execute functions that ensure speed and accuracy in fraud detection.
D) Creating a Model: When the algorithm is trained up to quality standards, financial institutions can utilize the model for filtering out authentic and fraudulent transactions to protect their usersâ€™ interests by performing optimum fraud detection. The model will have to be monitored and updated with necessary information and technologies on a constant basis to ensure consistent functionality and performance with time and safeguard against threats on a real-time basis.
Benefits of Utilizing Machine Learning For Fraud Detection
1) Scalability: On a daily basis, a large number of financial transactions take place at a volume and a speed that is impossible for manual processing to handle alone. Machine learning facilitates instant payment processing to filter out fraudulent transactions of large volumes. With illegal schemes becoming more sophisticated and harder to detect among innumerable transactions, ML with its self-learning abilities provides strong protection against these activities. With more exposure to these large transactional data sets, it is able to refine its procedure in filtering suspicious elements through increased familiarity with patterns and relationships found in financial transactions.
2) Faster Detection: When a financial transaction takes place, time is of absolute essence in payment processing as digital payments happen over a matter of seconds. Machine learning makes it possible for faster fraud detection with automation abilities that perform instant processing of financial transactions of huge volumes. It performs fast anomaly detection along with other techniques that filter out transactions containing suspicious elements. Along with its real-time filtering capabilities, it constantly improves its fraud detection abilities through self-learning algorithms that enhance its precision and speed with time and experience.
3) Higher Precision: Financial institutions have to deal with fraudulent activities of varying nature and keep up with rapid advancements in technologies to constantly safeguard their users against increasing threats. With exposure to larger datasets of financial transactions, machine learning algorithms are able to improve accuracy with greater experience and take necessary actions to stop illegal activities from happening and causing financial damage to their users. From identifying patterns and similarities of suspicious transactions in large volumes, the algorithms become more precise in filtering out fraudulent activities and are able to stop the process of letting digital payments from taking place. This helps in forming a safe barrier between financial institutions and their users from criminals.
4) Cost Efficiency: Payment processing is an arduous and near-impossible task for manual involvement as it requires constant monitoring of vast amounts of transactions taking place every day. Machine learning for fraud detection provides the necessary digital infrastructure for establishing robust security without having to employ a large number of teams to deal with tiring and monotonous tasks. By maintaining speed and accuracy in processing innumerable transactions, it helps financial institutions to be cost-efficient and stop financial leakage from taking place.
ML-based approach for fraud detection is undoubtedly one of the measures that can effectively guard against activities aiming to cause financial damage and inconvenience to financial institutions and their users with the help of constant monitoring and filtering abilities. They eliminate the possibility of human error in missing out on illegal financial activities of subtle and prominent impact that can cause huge negative consequences. With technology gaining a significant role in payment dealing, infrastructure that can provide a secure and reliable platform for carrying out transactions is also required. Machine learning for fraud detection helps to fill in the requirement with self-learning algorithms that ensure fast, accurate and cost-efficient solutions that are highly adaptable for unexpected situations through training. Financial institutions and companies like Danske Bank, Capgemini etc have been successfully employing machine learning models to counter financial threats and maintain reliability for its operations and customers. If you too are looking for ML-based approaches for ensuring security for your enterprise, we can assist you with customized solutions fit for your business needs. You can contact us here, and our experts will get back to you in 24 hours.