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.
- A sim swapping fraud in Europe was detected by an AI and saves 3.5 million euros and save 100+ victims and also caught 26 fraudsters.
- A teenager had been caught hacking Twitter who had fraud more than $110 Thousand and the fraud detector detect those teens and got the money back.
- The Wirecard Meltdown fraud detected by an AI has saved 70.74 million euros
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. Only 895 fraud cases are reported in the United States and Canada which is about 46% of all. More than 300,000 frauds were reported which costs 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, identity crime costs $1.6 billion each year, with the majority of about $900 million being lost by individual companies through credit cards.
- 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.
Nowadays a 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.
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 – That can erect patterns, clusters, and classes of suspicious behavior.
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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 beneath.
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
With accounting page driven gauging measures, there are cutoff points to the number of information sources and how much information can be processed and burned-through inside anticipating models. AI devices can significantly upgrade the volume and sorts of information that can be utilized on the grounds that the apparatuses can hold more information and process it quicker than people. Through this way we can have the better chance to hedge between the best and worst case scenario.
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 accomplice 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.
To summarize, occupations that are comprised of repeatable, precise errands have higher danger of being mechanized than those that require judgment, examination, and relationship building abilities. Thus, in the event that your present place of employment regularly expects you to show those attributes, at that point you need not concern. For Finance and Accounting, AI and robotization are viewed as suitable answers for successfully managing consistency and danger challenges across different areas. To stay serious, organizations are moving from work exchange and seaward unrest to mechanization insurgency. Man-made intelligence speaks to an occasion to decrease the weight on money experts, especially around conventional monetary exercises, for example, exchange handling, review and consistency. These exercises in their present structure keep money from being more essential colleagues.
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