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Fraud detection and prevention industry

Fraud detection and prevention industry 1


By Rahul Kumar Pandey, ICT, Adroit Market Research

Widespread use of digital transformation has eased the way of doing transactions whereas growing frauds and its changing landscape is a by-product that comes with it. There are several measures in place to deal with such activities and these measures are continuously becoming advanced as we are moving ahead and there will be a lot of developments in the coming years using new-age technologies. So, let’s understand what it is and why it matters.

Fraud is an intentional action to acquire illegitimate gains. It involves improper payments, money laundering, terrorist financing, public security, and cybersecurity. Traditionally, administrations had to take a fragmented approach to prevent fraud due to the non-availability of technological advancements. The method included was using business rules and elementary analytics to look for irregularities and thereby generating alerts. Cross-reference of the data was impractical without automation thereby leaving investigators with manually monitoring the transactions without real-time fraud detection. This has created an industrywide space for frauds, particularly in BFSI and healthcare.

Fraud detection is the collection and analysis of the information to find possible fake conduct before it occurs. This begins with discovering links between devices, items, and areas, and mapping that information to a particular client, an individual, as well as representatives. This methodology is effective in connecting tremendous amounts of information with the entirety of the individuals who some way or the other associated with that information.

Enterprises are now deploying new-age technologies to fight against fraud and its changing landscape. This involves the use of advanced analytics for predictive and adaptive measures. Inclusion of Artificial Intelligence (AI) based on machine learning and Big Data provides real-time monitoring and risk profile analysis to slash the fraud risk thereby turning the tide of losses.

Todays’ world and fraud detection

Frauds have constantly existed all through mankind’s history yet in this period of advanced innovation, the extent and the size of money related frauds is turning out to be wide-extending, from card payments to insurance claims. The developing complexities of state-supported terrorism and proficient lawbreakers are getting more difficult to comprehend, uncover, and predict. The fraudsters are getting more imaginative. As a result, they have created advanced strategies, thereby making prevention fundamental to keep steady over these changing methodologies. In the current scenario, fraud identification includes an exhaustive way to deal with correlated information to identify the irregularities.

Across the globe, assessed losses in business associations and other entities can go up to 3-5% of their incomes because of fraudulent transactions. Though this may not sound a great deal, however, it has a significant impact in terms of finances. In 2018, the UK witnessed a loss of over 1.3 billion dollars through frauds and scams. Similarly, during the financial year 2018-2019, Indian banking fraud reported was over INR 700 billion which over 70% higher than the previous financial year losses. The total fraud losses reported in 2019 across the globe was over 40 billion dollars.

The most frequently reported fraudulent activities in 2019 were leaks in internal information, data theft, reputational damage by third-party relationships, internal frauds, and disruptions due to sanctions, tariffs, etc. Adversarial social media activity and IP theft were also among the frequently reported fraud incidents. Albeit numerous associations took measures to counter frauds, it would never be eliminated. Hence the objective is to limit its effects.

Thinking about these conditions, the companies need to adopt instruments, procedures and best practices to recognize and stop a variety of frauds, rapidly and precisely while improving client and resident involvement. Enterprises, need to capture and bring together all accessible information types from across divisions or channels and consolidate them into the scientific procedure. Some of the other measures are continuously screening transactions, social networks, and high-risk irregularities, and implement behavioral analytics for real-time data-driven decision making. Implementation of enterprise-wide data analysis through data visualization at all levels, including investigative work process improvement.

Business and governments the same have incorporated advancements like information visualization and AI to significantly decrease, anticipate, and even prevent the affordable and reputational repercussions of frauds. Organizations know about the commonness of fraud in the market place and the magnitude to which fraudulent activity has an impact on their business.

The future preparation and data science

Rising digitization and rising utilization of smartphones are additionally adding to the development of false exercises as individuals are moving from conventional payment frameworks to the portable wallets, UPI, and online payment systems. Online activities among consumers reflect the widespread embrace of digital commerce as a way to purchase goods and services and conduct personal banking. This is resulting in intensifying frauds across the globe. In the coming years, several sectors will face an increased risk of fraud and threats predominately in the areas of internal information leak, large-scale cyber-attacks, financial crisis, data theft, reputational damage due to third party relationships, changes in trade agreements and IP theft.

In the coming years, the increasing complexity of threats and fraudulent activities will demand more advanced technologies and sophisticated solutions to limit its impact. The fraud detection and prevention technology need to be equipped with learning from complex data patterns. It should utilize decision-making models to effectively manage false positives and detect network relationships to see an all-encompassing perspective on the action of fraudsters and lawbreakers. Integration of new-age technologies such as machine learning, deep learning, and neural networks coupled with prevalent methodologies including logistic regression, self-organizing maps, random forests, and ensembles has demonstrated to be more precise and powerful than traditional rule-based approach.

With ever-expanding online transactions and the creation of an enormous volume of customer information, AI has been progressively observed as a compelling instrument to identify and counter frauds. However, there is no particular technique, that works for a wide range of fraud discovery issues in every industry. The issue is distinctive for each situation and each industry. In this manner, each arrangement is deliberately custom-made inside the space of every industry.




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