Case Study on Fraud Detection: How Machine Learning Systems Help Reveal Fraud
“ We’re living amidst an explosion of risk related to fraud, money laundering, terrorist financing, and data privacy.”
Janet Yellen (2021), US Treasury Secretary.
Here are some brief statistics where fraud detection worked :
- Highmark Inc. has generated millions of dollars in savings related to fraud, waste, and abuse which is approximately $250 million in 2019. And they also remarkably saved over 850 million dollars in the last five years using fraud detection AI (Artificial Intelligence).
- Microsoft is spending 33% of its annual revenue each year to protect cloud storage from any sort of fraud and it grosses up to 44.5% profit added to their annual.
- AI detects sim swapping fraud in Europe, saving 3.5 million euros, rescuing 100+ victims, and capturing 26 fraudsters.
- The fraud detector caught a teenager who hacked Twitter and committed fraud exceeding $110 Thousand, successfully recovering the stolen money.
- The Wirecard Meltdown fraud detected by an AI has saved 70.74 million euros
Statistics on Detected Fraud Cases and Financial Impact :
According to the Association of Certified Fraud Examiners (ACFE), the detected fraud cases are around 10% of all cases. But, there are more than 85% accounted cases in which the median loss is approximately $950,000. Moreover, nearly one-third of the fraud cases occurred because of insufficient internal controls. Individuals in the United States and Canada reported only 895 fraud cases, accounting for approximately 46% of the total. The reported frauds exceeded 300,000, resulting in a cost of over $1.4 billion. Cybercrime costs approximately $ 600 billion of the global economy, which translates to 0.8% of total global GDP.
- In 2017, United Kingdom fraud cases hit ￡2.11 billion. Cases rise from 212 in 2013 to 577 in 2017, with every case worth more than ￡50,000. UK loses over ￡190 billion per year which is approximately 9% of the UK’s GDP.
- In Australia, individuals and companies lose approximately $900 million each year through credit card fraud, contributing to the total cost of identity crime, which amounts to $1.6 billion annually.
- A major fraud happened by breaching systems at TJX companies that exposed 45.6 million credit card information between 2005 and 2007.
- In 2012, Adobe Systems was compromised by a hacking act that cost 40 million of the payment card information.
- Unauthorized financial fraud losses across payment cards and remote banking totaled ￡844.8 million that was acted by a third-party group. Though banks and card companies prevented more than ￡1.6 billion using fraud detectors in 2018.
The Growing Threat of Modern Fraud and the Role of Fraud Detection AI :
Nowadays fraud can be performed anywhere, anytime, from any place using modern technologies. Bank money laundering, credit card fraud, etc. are now very simple for digital thieves to perform far away from the crime scene. Most fraud occurs through the internet by performing unauthorized uses of private information such as bank account details, credit card details. Hence, fraud detection AI (Artificial Intelligence) is introduced to prevent those frauds. In other words, fraud detection is a way to protect money from false pretenses.
Mainly the problem of not using a fraud detector is huge financial risks. Financial risk ends at losing money or hampering one’s reputation in the market. So, one can prevent any sort of financial risk by just adding a fraud detector as a solution to their system.
The Versatility of Fraud Detection and Common Types of Frauds Detected :
A fraud detector can detect frauds occurring often through the internet. Fraud could be anything. Such as credit card fraud, bank money transferring fraud, insurance fraud, etc. involving exaggerating losses of money. There are many ways a person can perform a fraud activity that can be difficult to detect. Activities can be reorganization, downsizing, moving to new information systems, or encountering a cybersecurity breach. Frauds are actually a typical act that repeats simultaneously, making searching for patterns. A data analyst can prevent insurance fraud by applying algorithms to detect patterns and anomalies.
Rather, there is one type of fraud that happens most. Which is bank account takeover fraud, where someone steals access to the victim’s bank account using bots. Other banking-related frauds occur using malicious applications, use of false identities, money laundering, credit card fraud, mobile fraud.
Fraud in insurance includes diversion fraud, which is premiums’ embezzlement, churning fees, that are executed by stockbrokers for extra commissions. Frauds in government federal agencies such as departments of health and human services, transportation, education. These frauds are executed during the unnecessary billing procedure and overcharging in every step.
Though fraud detection is a hard job to accomplish by any sort of algorithms, there are some AI (Artificial Intelligence) techniques that are used to detect fraud in recent times.
- Data mining – This can classify groups and segments of data to search through millions of transactions to find patterns and detect fraud.
- Pattern recognition – This can erect patterns, clusters, and classes of suspicious behavior.
We are the company providing a great Fraud Detection System for you.
Still, Have any Questions or Doubts or Queries?? Why don’t you message us for more clearance?,
Advantages of Machine Learning in Financial Forecasting
Using AI to robotize the monetary estimating measure presents a few remarkable advantages for senior money heads and their groups. The key advantages are summed up below.
1. Capacity to Produce More Accurate Forecasts, Faster
As referenced before, AI empowered estimating can free monetary gauging of the concentrated work of gathering and accommodating information. The apparatuses can be designed to gather and accommodate extremely enormous informational indexes in a mechanized style. In addition, AI apparatuses can assist with deciding business drivers and enormously diminish estimate blunder. AI calculations are intended to gain from the information after some time and foresee which drivers have the best effect on monetary execution. After some time, the model turns out to be more precise and produces figures all the more rapidly.
2. Hedge between best and worst case scenario
By utilizing AI tools for forecasting, the limitations of traditional accounting-driven measures can be overcome. These tools enable the inclusion of a greater volume and variety of data, surpassing what humans can handle. AI systems have the capacity to store and process vast amounts of data at a faster pace. This empowers organizations to make more informed decisions, enhancing their ability to navigate between best and worst-case scenarios. With AI-driven forecasting, there is an opportunity to leverage the power of data and optimize decision-making processes for better outcomes.
3. Empowering Value-Adding Activities
Customary estimating measures ordinarily expect examiners to invest the vast majority of their energy accommodating and gathering information as opposed to chipping away at esteem added investigation and collaborating with the business. Utilizing an AI answer for production at any rate a standard estimate can help investigators move away from these commonplace assignments and spotlight on understanding operational drivers, key business occasions, and microeconomic and macroeconomic elements that may affect the business, carrying those experiences into the determining cycle. Utilizing AI can eventually help monetary investigators accomplish all the more intimately with the business and backing dynamic.
The Emergence of AI in Accounting and Finance
There are sure businesses that are generally helpless to the effects of AI. The most referred to report from NPR predicts that:
- Bookkeepers have a 97.6 percent possibility of seeing their positions computerized.
- Accountants and examiners have a 93.5 percent possibility of seeing their positions mechanized.
- Financial examiners have a 23.3 percent possibility of seeing their positions mechanized.