What is AI?
Artificial intelligence refers to the simulation of human intelligence in machines. Specific applications of AI include expert systems, natural language processing, speech recognition, and machine vision. Algorithms often play a vital part in the structure of artificial intelligence, where simple algorithms are used in simple applications, while more complex ones help frame solid artificial intelligence.
Difference between AI & Machine Learning
Artificial intelligence and machine learning are the parts of computer science that are correlated with each other. These two technologies are the most trending technologies which are used for creating intelligent systems. Although these are two related technologies and sometimes people use them as a synonym for each other, still both two different terms in various cases.
On a broad level, we can differentiate both AI and ML as:
AI is a wide-ranging branch of computer science concerned with building intelligent machines capable of performing tasks that typically require human intelligence.
Can machines think? — Alan Turing, 1950
Turing was a founding father of artificial intelligence and of modern cognitive science, and he was a leading early exponent of the hypothesis that the human brain is in large part a digital computing machine. He theorized that the cortex at birth is an “unorganized machine” that through “training” becomes organized “into a universal machine or something like it.” Turing proposed what subsequently became known as the Turing test as a criterion for whether an artificial computer is thinking (1950).
Machine Learning
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Machine learning algorithms use historical data as input to predict new output values.
You may also like to read: Cheat Sheets for AI, Machine Learning, Deep Learning, Big Data & Neural Networks
How AI works for fraud prevention?
Digital scam rates have ramped up in numerous sectors over the past year, with the financial area seeing its biggest spike in March 2020. Experts estimate that anywhere from 79 percent to 90 percent of these assaults were ATOs, which consist of criminals presuming control over consumer accounts for a variety of functions, such as using their settlement info to make fraudulent acquisitions or stealing their individual information and also marketing it on dark web industries.
The future of AI-based fraud prevention relies on the combination of supervised and unsupervised machine learning. Supervised machine learning excels at examining events, factors, and trends from the past. Historical data trains supervised machine learning models to find patterns not discernable with rules or predictive analytics. Unsupervised machine learning is adept at finding anomalies, interrelationships, and valid links between emerging factors and variables.
Artificial intelligence has enabled banks to more accurately detect fraud while minimizing false positives. The solution works by understanding the normal payment behavior of each customer and using behavioral risk models to detect fraudulent transactions that are outside the norm. For instance, scams are detected on customer accounts due to a combination of unusual factors such as unusual amounts, new beneficiaries, new receiver bank country, new currency, new operation type, etc.
Limitations of AI
- High cost of implementation
- Can’t replace humans
- Lacks creativity
- Risk of unemployment
Setting up AI-based machines entails huge costs and given the complexity of engineering. These AI-based software programs require frequent upgrades in order to cater to the requirements of the changing environment as the machine needs to become smarter by the day.
It is beyond a shadow of a doubt that devices do much more successfully as contrasted to human beings. But also then it is almost difficult to replace human beings with AIs, at the very least in the future, because you can’t construct human intelligence in a machine as it is a gift of nature. So, no matter just how smart a maker can end up being, it can never ever change a human.
As mentioned above– AIs are not built for creative pieces of work. So, it should be crystal clear by now that creative thinking or imagination is not the strength of AIs. Although they can aid you in creating and also developing something unique, they still can’t take on the human brain. Their creative thinking is restricted to the innovative capability of the individual that programs as well as commands them.
With rapid advancement being made in the field of AI, the question that afflicts our instinctive mind is– will AI change human beings? Honestly, I am not exactly sure whether AIs will bring about higher joblessness or otherwise. But AIs are most likely to take over most of the repetitive jobs, which are mostly binary in nature as well as include minimum subjectivity.
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Conclusion
As long the modern-day globe is overwhelmed with card-not-present transactions online, the Banking as well as Retail markets are under threat and encounter numerous fraudulence instances. Email phishing, settlement fraud, identity burglary, record imitation, and also fake accounts contribute to the high degree of criminal attacks on at-risk individuals’ data as well as result in information breaches. As old rule-based algorithms for fraudulence detection fade right into the past, new superior techniques based on Machine Learning formulas for scam detection and prevention are bringing higher value to services with their real-time work, rate, and also effectiveness.
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