Ever since the payment systems existed, there were always people who would find sophisticated ways to get to someone’s finances illegally. It has become a major problem in the modern era when all transactions can easily be completed online with only entering your credit card information. Even in the 2010s quite a lot of American retail website users suffered from online transaction frauds until the two-step verification came into power while shopping online.
However, credit card frauds are still a challenge for money transaction platform owners or small to global retail services and hundreds of online frauds keep popping up daily as long as unauthorized card transactions hit the record amount of 16.7 million victims in 2017 (as reported by Javelin Strategy & Research). Additionally, according to the Federal Trade Commission (FTC), the number of credit card fraud claims in 2017 overcame the previous year’s number of frauds by 40%. There were around 13,000 reported cases in California and 8,000 for Florida, which is the most per capita credit card fraud claims). The future number of transactions will only increase and exceed approximately $7.2 million by 2020 where card-non-present (CNP) transactions online or with mobile phones will make the majority of that number.
Credit Card Fraud Detection with Machine Learning
There are a few ways to implement automated fraud detection:
- Off-the-shelf fraud risk scores pulled from third-parties (e.g. from LexisNexis or MicroBilt)
- Predictive machine learning models that learn from prior data and estimate the probability the transaction is fraudulent
- Business rules that set conditions that the transaction must pass to be approved (e.g. no OFAC alert, SSN matches, below deposit/withdrawal limit, etc.)
Among these Fraud Detection and Prevention techniques, predictive Machine Learning models are the one that belongs to smart Internet security solutions. There are several steps within each AI Fraud detection and prevention development process. The first is Data Mining that implies classifying, grouping and segmenting data to make a search through millions of transactions to find patterns and detect fraud. After the patterns are discovered, there is a Pattern Recognition phase which can detect classes, clusters, and patterns of suspicious behavior. Machine Learning itself here represents the choice of a model/set of models that best fit to a certain business problem. For example, the Neural Networks approach helps to automatically identify characteristics found in fraud, which is the most effective if you have a lot of transaction samples.
Once the machine learning-driven Fraud Protection module is integrated into the E-commerce platform, it starts tracking the transactions. Whenever a user requests a transaction, it is being processed for some time and depending on the level of predicted fraud probability there are 3 kinds of possible outputs:
- If the probability is less than 10%, the transaction is allowed;
- If the probability is between 10% and 80%, additional authentication factor (one time SMS code, Fingerprint, Secret Question) should be applied;
- If the probability is more than 80%, the transaction is frozen, so it should be processed manually.
Using Machine Learning for fraud prevention also has its pros and cons as any other security means. Being a potent tool to identify and block frauds, and often one of the most accurate, it improves itself overtime when it gets additional data. It means that you will have a continually self-improving mechanism that spots frauds with newer patterns.
Although Fraud Detection with Machine Learning is surely an advantageous method, it still has some restrictions that should be paid attention to, before you implement it on a platform.
Firstly, training high-quality machine learning models requires significant internal historical data. That means if you do not have enough previous fraud and normal transactions, it would be hard to run a Machine Learning model on it because they are very demanding for information.
Secondly, models may be subject to bias based on the nature and quality of historical data. This statement means that if the platform maintainers did not collect and sort the data neatly and properly, or even mixed the information on fraud transactions with the information on normal ones, that is likely to cause a big bias in the results of the model.
But if you have enough data that is well-structured and not biased, if your business logic is paired nicely with the Machine Learning model, the chances are very high that fraud detection will work best for your safety.
Read full article here: https://spd.group/machine-learning/credit-card-fraud-detection/
Improved operations, efficient cost management vs. focus on profitability:Banks essentially have to make a profit to survive, and today, banks face significant pressure on their margins.
Automation of about 80% of repetitive work processes helps officers dedicate their time to value-added operations that require a high level of human intervention like product marketing.What we need now is not just empowering of banks by automation, but making the entire system intelligent enough to beat the newly emerging FinTech players.
It can help in costly and error-prone banking services like claims management by drastically reducing the time spent in reading or recording client information.For instance, JPMorgan Chase’s COiN reviews documents and extracts data from 12,000 documents (which, without automation, would require more than 360,000 hours of work) in just seconds.Lending:A minuscule percentage of the Indian population has an idea of credit.
It is also an annoying task for banks to analyze an individual’s creditworthiness due to the lack of credit history.The use of Big Data and Machine Learning to analyze spending patterns and behavioral data of a customer over 10,000+ data points can help banks have an insight into the customer’s creditworthiness.
In the case of SME and corporate loans, AI simplifies the complex and critical borrowing process, identify the potential risks in giving the loan by analyzing market trends, prospect’s behavior, and identifies even the slightest probability of fraud.Risk Management And Fraud DetectionThe Punjab National Bank scam exposed the banking sector to an enormous amount of risk and shook the regulators, financial and stock markets, and the banking industry.
AI solutions can also be a game-changer by detecting insider trading that leads to market abuse.Insurance underwriting and claims:In this era of bancassurance, customers are more likely to come to banks rather than visit insurance agencies.
AI, coupled with the rich data sets available in the financial services sector, provides organizations with the means of defending their businesses and defeating criminals.
Let’s have a look at how.
Read Full Article : https://bit.ly/37FH3em
Zencode was founded in 2013 as an IT outsourcing company.
We set up our first Delivery Centre at Chennai, India in 2014.
We outgrew the garage to a full fledged office within 2 years.Along the way, we also expanded our market footprint to Hong Kong and setup our AI & Blockchain practice at Bangalore, India.Today, we can help you build your IT platforms from start to finish.
From design to coding to implementation and support .
Zen is a business application that will revolutionise the way a company does things; A marketplace that brings various people together; A custom business application for your business; A mobile application that uses crowdsourcing to bring convenience to customers; or a myriad of potential new industries, ask Zencode to help you with the implementation.Email Address Sales : [email protected] HR : [email protected] us: BANGALORE III & IV Floors, NCR Complex 580/B, Sector 6, HSR layout, Bengalore-560102
Artificial Intelligence is also known as machine intelligence, where machines mimic the intelligence of the human mind and actions.
The primary purpose of artificial intelligence is to make rational decisions according to the circumstances.
The correct implementation of AI can carry more complex tasks with less time.
Artificial intelligence has received an upward trend of growth, leading to the development of human society and multiple industries, and artificial intelligence will continue to grow in promising ways for consumers and various businesses.As 2020 has not been a great year so far because of COVID-19, artificial intelligence has played a massive role in combating the pandemic induced situations.
Every ounce of technological advances and innovations helped us to fight the pandemic.Every organization is finding new ways to operate effectively and to serve their customers as social distancing measures remain in place.
These chatbots have provided the answer to almost every possible question, from evaluation to exercise without putting the human resource at risk.Machine learning and artificial intelligence have helped researchers to analyze a large volume of data to take preventive measures.
There are several ways organizations deal with the contracting process.
Few manage it with word-based drafting and redlining coupled with storing in repositories like Drop Box or SharePoint and some deploy automated contract management systems for this process that also include collaboration.https://blogs.docskiff.ai/top-5-ingredients-of-ai-based-contract-analytics-platforms/
The most advanced technology/concept of this era might be Artificial Intelligence but everyone wonder how it works.
Artificial Intelligence is like introducing a brain to the machine which can be used while the system need to make some decision or process data.
How to introduce this brain to the machine?
How efficient the machine will make use of this brain?
Let’s look at the answers for this question?
