Guest Editorial by Gil Artmoore
While the number of identity fraud cases has steadily declined over the past few years, the financial burden borne by the victims has increased.
According to Javelin Strategy and Research, American Victims of identity theft lost $1.7 billion in 2018.
This is double the amount that was lost in 2016.
As malicious actors step up their game and adopt new strategies to steal personal data, you should consider adopting high-tech solutions such as Artificial Intelligence (AI) to protect your business from privacy breaches and identity fraud.
AI has lots of applicability in this area ranging from net identify verification processes to anomaly detection.
Below are some of the ways you can employ AI in your business to reduce the chances of identity fraud…
Do Large-Scale Customer ID Verification in Real-Time
Many online businesses are required by law to verify the personal details of their customers.
However, ID verification can cause friction for both the customer and the business because drawn-out ID verification processes hurt customer experience.
Customer experience is a key business metric that should never suffer under any circumstances.
In fact, according to Walker, a consulting firm, customer experience is poised to overtake product and price as a key brand differentiator by 2020.
To alleviate the customer experience problem when doing ID verification, businesses can utilize the power of Artificial Intelligence to automate processes.
Real-time ID verification is possible today because machine learning, AI, and computer vision can help businesses acquire and extract data from ID documents then verify the authenticity and accuracy of the data in real-time.
Typical ID verification processes that utilize machine learning and artificial intelligence take the following format:
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A customer takes a picture of their government-issued ID card
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The image is converted into computer-readable text
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The customer takes a video or photo selfie and the system verifies if the face recognized matches the one extracted from the ID card
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The customer takes a liveness detection test where the AI system verifies if the video was taken in a live setting.
This application of AI will allow your business to catch fake IDs, meet compliance requirements and mandates, and streamline customer onboarding processes without hurting customer experience.
Identify Threats through Pattern Recognition
The nature of user threats such as ransomware attacks and phishing are evolving as cybercriminals apply deep learning and machine learning technologies.
You must update your business’ cybersecurity protocols to match this reality.
The best way to protect your business against these threats is to use security automation and predictive analytics.
Both methods rely heavily on machine learning and artificial intelligence to recognize regularities in data.
Predictive analytics uses AI to monitor large data sets so that a system can record patterns and be better positioned to identify unusual activity.
The automation frees up human labor and shortens the time frames within which new threats are detected.
Pattern recognition, which is basically the recognition of regularities in data, identifies threats by comparing live activity with known patterns and models that are characteristic of illicit activity.
The goal is to monitor unusual data use and stop threats in their tracks.
Improved Large-Scale Data Analytics
Businesses that rely on humans to do mass data analytics are at a tremendous disadvantage today.
Processes that rely on humans are largely rules-based and this makes them prone to having loopholes. Cybercriminals only need to identify a single loophole once to cause havoc.
The use of Artificial Intelligence allows businesses to adapt their rules faster based on incoming data and computer-human interactions.
Further, using algorithms makes the changes consistent and reliable even when large volumes of data are involved.
As companies grow, their systems and processes also expand thereby creating new points of weaknesses that can be exploited by cybercriminals.
According to a case study done by Ayasdi in 2018 that involved HSBC Holdings, beyond a certain level, instances of fraud correlatively increase with increases in the amount of data a bank has to process.
Traditional data analytics and reviewers are ill-equipped to fend off attacks when large volumes of data are concerned given the technical competence of today’s cybercriminals.
Detecting malicious activity through large-scale data analytics is, therefore, more effective when AI is employed.
Closing Thoughts
The technical solutions for detecting fraud made possible through AI are effective and scalable.
This allows for easy automation to free up labor for activities that provide businesses with greater returns on investment.
Nonetheless, you should avoid full automation and retain some trained professionals to ensure greater accuracy and prevent bad customer experience.
There are instances where software can flag valid IDs because of wear and tear or fail to authenticate legitimate customers because of data misinterpretations.
When such failures occur, a trained professional can make corrections and even train the computer to make better computations in the future.
By inputting new data into the system, the trained professional improves the model.
This, in turn, leads to greater accuracy and greater effectiveness at detecting fraud while also ensuring that customers enjoy a wonderful experience when using your business’ products and services.
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