There was a time, just a couple of decades ago, when researchers in psychology and health always had to engage with people face-to-face or using the telephone. The worst case scenario was sending questionnaire packs out to postal addresses and waiting for handwritten replies. So we either literally met our participants, or we had multiple corroborating points of evidence that indicated we were dealing with a real person who was, therefore, likely to be telling us the truth about themselves. Since then, technology has done what it always does – creating opportunities for us to cut costs, save time and access wider pools of participants on the internet. But what most people have failed to fully realise is that internet research has brought along risks of data corruption or impersonation which could be deliberately aiming to put research projects in jeopardy.

Banks worldwide are expected to spend an additional $31 billion on artificial intelligence (AI) embedded in existing systems by 2025 to reduce fraud, according to a report. Similarly for banking executives worldwide, fraud management is featured strongly as a priority, the IDC report mentioned. "In the process of coming up with digital products and services, new channels, and new payment methods, businesses might be overestimating the adequacy of their current defense mechanisms against fraud," said Michael Araneta, Associate Vice President, IDC Financial Insights. "What worked well before simply would not be enough now in the more digital world of business. There needs to be a constant upgrade of fraud management capabilities," Araneta added.

Banks worldwide are expected to spend an additional $31 billion on artificial intelligence (AI) embedded in existing systems by 2025 to reduce fraud, according to a report. Similarly for banking executives worldwide, fraud management is featured strongly as a priority, the IDC report mentioned. "In the process of coming up with digital products and services, new channels, and new payment methods, businesses might be overestimating the adequacy of their current defense mechanisms against fraud," said Michael Araneta, Associate Vice President, IDC Financial Insights. "What worked well before simply would not be enough now in the more digital world of business. There needs to be a constant upgrade of fraud management capabilities," Araneta added.

The race is on to reduce fraud and continue improving payment flows. Artificial intelligence (AI) offers a winning strategy, says Chalapathy Neti, head, AI and machine learning platform, Swift. AI is out of the lab and already well on its way to delivering smarter tech solutions in our daily lives. Just look at the way Amazon and Netflix use machine learning algorithms to continually serve us fresh content and products based on our previous behaviours. We get a better, more personalised experience while they strengthen their business models.

Leading e-commerce companies are training their cybersecurity analysts on transaction fraud detection systems and collaborating with vendors to detect identity spoofing and the use of stolen privileged access credentials. Fremont, CA: Cyberattacks are becoming more sophisticated, targeting multiple threat surfaces at the same time and employing a wide range of techniques to avoid detection and gain access to valuable data. Bad actors' preferred attack strategy is to use various social engineering, ransomware, phishing, and malware techniques to obtain privileged access credentials in order to circumvent Identity Access Management (IAM) and Privileged Access Management (PAM) systems. Detecting Transaction fraud – According to CISOs, the pandemic's effects on e-commerce sales are the primary motivator for investing in AI and ML-based transaction fraud detection. Transaction fraud detection is intended to provide real-time monitoring of payment transactions through the use of machine learning techniques to identify anomalies and potential fraud attempts.

Machine learning is a field of science that offers machines an ability to understand data and carry out processes just as a human would do. The ML technology uses complex algorithms to analyze large data sets and find data patterns that help in business decisions. This is why machine learning can detect fraud in the system easily. It is, in fact, used for various other purposes such as spam detection, product recommendation, image recognition, predictive analysis, etc. Gartner predicted that by the year 2022, the machines would be analyzing 50% of the data, which is only 10% more from the present scenario. Since machines are far better at detecting patterns, ML can analyze huge sets of data in one chance and find fraud-related behavior through cognitive technology.

PointPredictive recently added to its roster of experts tasked with helping auto finance companies combat fraud and other risks to their operations. PointPredictive announced that Mike Kennedy has joined the company as vice president of analytics to spearhead the growing team of data scientists in its San Diego offices. Kennedy has more than 20 years of machine learning experience across credit card, retail banking, small business and auto financing with a focus on leveraging advanced data science to stream-line lending decisions while driving down fraud and misrepresentation risk. Kennedy previously held key leadership positions at Mulligan Funding, Opera Solutions and Fair Isaac Corp. (FICO). "I am excited to embark on this opportunity with PointPredictive," Kennedy said. "The team is trailblazing new ground helping auto, mortgage and retail lenders leverage artificial intelligence.