One of the most common reasons organizations fail to realize significant improvements in risk management after implementing custom modeling solutions can be described with one phrase: Junk in. Junk out. This article discusses best practices as it relates to data management and other factors that are shown to improve performance when it comes to custom modeling and machine learning or artificial intelligence.
It’s not just breadth of data, but also quality of data, that is important. One of the biggest misconceptions about machine learning (ML) and artificial intelligence (AI) is that you can just flip a switch and let the technology work its magic.
While there is value in leveraging Artificial Intelligence for modeling and analytics to detect cyber threats and fraud, security professionals are still more likely to indicate that a human touch is more valuable. A recent survey found that half of organizations are making use AI or machine learning but 60 percent put more trust in findings verified by humans. Meanwhile, changing consumer patterns and the rush to work from home in response to the pandemic has likely led to higher rates of false positives.
This isn’t to say AI and ML are not important – they are. In the same survey, 65% said these tools allow them to focus more on preventing cyber attacks than before and 40% reported feeling less stress.
In mid-January IBM announced the acquisition of IRIS Analytics, a data modeling and analytics provider based in Germany that focuses on risk management for issuers and payment providers. This is IBM’s second acquisition in risk management for the financial services industry following their 2013 purchase of Trusteer.
IRIS Analytics was founded in 2007 and provides a data modeling and analytics platform with real-time risk scoring to support payment card and mobile payments. The acquisition will bolster IBMs fraud prevention and eCommerce services, complimenting their IBM Security Trusteer Advanced fraud protection solutions.
Real-time custom modeling can enhance Business Intelligence capabilities leading to not only improved risk detection, but an overall better understanding of your customers and business, which benefits other areas of the business outside of risk management. Many of the capabilities and features to look for when building or buying custom modeling solutions for risk management also contribute to the ancillary benefits a risk-focused custom modeling solution may offer in other departments or groups, such as marketing and front-end or user experience analytics.
This feature article from The Fraud Practice specifically discusses the critical capabilities organizations should build or look for if they want to leverage custom modeling solutions for effective Business Intelligence, both as it pertains to risk management and other aspects of a business.
The Fraud Practice is hosting a webinar at 1pm ET on December 4th accompanying the new white paper titled “Enabling Custom Modeling & Analytics for The Modern eCommerce Merchant” which will be released the same day.
The eCommerce market has continued to mature over recent years to the benefit of both consumers and businesses that transact in this channel. As a result new markets and services have been created while existing markets have evolved and grown. Not long ago, any business seriously considering custom models would have found it expensive, timely and somewhat difficult to put in place, so the concept of implementing custom models was left for large financial and retail organizations that had the resources to afford the cost and teams that could support the models. This is no longer the case today, however, as the maturation and growth of these services has made custom modeling and analytics much more accessible and attainable to a far greater population of eCommerce merchants.
All who register for the live webinar will automatically receive a copy of the new white paper from The Fraud Practice prior to the webinar start time.
Register for the webinar and white paper
More information about the white paper
In mid-September data analytics provider FICO announced they would acquire Adeptra Ltd., a privately held company based out of the UK offering cloud-based customer engagement and risk intervention solutions, for $115 million in cash.