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Keeping your customers happy is critical to success. And while reducing fraud is imperative, it shouldn’t detract from a positive customer experience. Here are 3 fraud detection and prevention strategies that can help you reduce fraud and protect (and retain) customers.

Believe it or not, 66% of consumers want to see some visible signs of security and barriers when accessing their accounts so they can be sure that a transaction is more secure. Other takeaways from our 2018 Global Fraud and Identity Report

Competition is fierce. Expectations are high. Navigating a new market can be profitable — if managed strategically. Consider these actionable insights when entering a new market:

While the marketplace struggles to manage the impact of fraud prevention, CIP routinely disrupts more than 10 percent of new customer acquisitions.

When developing a risk model, validation is an essential step in evaluating and verifying a model’s predictive performance. There are two types of data samples that can be used to validate a model.

A summary of common resampling techniques that can be used to create a robust model development and validation sample.

Digital credit offers and regulations. There’s no question today’s consumers have high expectations. As financial services companies wrestle with the laws and consumer demands, here are a few points to consider:

The key to data isn’t just accessing it. It’s interpreting it — and using it to make better decisions that benefit your business and your customers.

some synthetic identities are being used for purposes other than fraud. Here are 3 types of common synthetic identities and why they’re created

Model validation is essential in evaluating and verifying a model’s performance during development before finalizing design and implementation.

The business case for identity verification and risk assessment tools is most compelling when it includes a broad range of both direct and indirect factors. Here are 3 indirect measures we suggest you consider:

Data is a part of a lot of conversations in both my professional and personal life. Everything around us is creating data – whether it’s usable or not is a business case for opportunity. Think about how many times a day you access the television, your phone, iPad or computer. Have a smart fridge? More data. Drive a car? More data. It’s all around us and can help us make more informed decisions. What is exciting to me are the new techniques and technologies, like machine learning, artificial intelligence and SaaS-based applications, that are becoming more accessible to lenders for use in managing their relationships with customers. This means lenders – whether a multi-national bank, online lender, regional bank or credit union – can make better use of the data they have about their customers. Let’s look at two groups – Gen-X and Millennials – who tend to be more transient than past generations. They rent not buy. They are brand loyal but will flip quickly if the experience or their expectations aren’t met. They live out their lives on social media yet know the value of their information. We’re just now starting to get to know the next generation, Gen Z. Can you imagine making individual customer decisions at a large scale on a population with so many characteristics to consider? With machine learning and new technologies available, alternative data – such as social media, visual and video data – can become an important input to knowing when, where and what financial product you offer. And make the offer quickly! This is a stark change from the days when decisions were based on binary inputs, or rather, simple yes/no answers. And it took 1-3 days (or sometimes weeks) to make an offer. More and more consumers are considering nontraditional banks because they offer the personalization and speed at which consumers have become accustomed.  We can thank the Amazons of the world for setting the bar high. The reality is - lenders must evolve their systems and processes to better utilize big data and the insights that machine learning and artificial intelligence can offer at the speed of cloud-based applications. Digitization threatens to lower profits in the finance industry unless traditional banks undertake innovation initiatives centered on better servicing the customer. In plain speak – banks need to innovate like a FinTech – simplify the products and create superior customer experiences. Machine learning and artificial intelligence can be a way to use data for making more informed decisions faster that deliver better experiences and distinguish your business from the next. Prior to Experian, I spent some time at a start-up before it was acquired by one of the large multi-national payment processors. Energizing is a word that comes to mind when I think back to those days. And it’s a feeling I have today at Experian. We’re taking innovation to heart – investing a lot in revolutionary technology and visionary people. The energy is buzzing and it’s an exciting place to be. As a former customer of 20 years turned employee, I’ve started to think Experian will transform the way we think about cool tech companies!

Market trends and insight from Q1 2018. The economy remains steady as we transition from 2017. Keep an eye on inflation and interest rates.

According to our State of Alternative Credit Data research, more lenders are using alternative credit data to determine if a consumer is a good or bad risk

A thoughtful segmentation analysis contains two phases: generation of potential segments, and the evaluation of those segments.