Data & Analytics

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

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!

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

The first full day of Vision 2018 featured in-depth talks on alternative credit data, enhanced credit marketing, faster decisioning, fraud and identity protections and the latest in tech innovation.

In an all-new report, Experian dives into “The State of Alternative Credit Data,” providing in-depth coverage on how alternative credit data is defined, consumer personas, and how this data complements traditional credit data files.

Experian introduces new trended attributes to help lenders better serve consumers across the credit life cycle

Consumer disputes aren’t going away, but understanding the reported data and metrics behind disputes can help data furnishers minimize them and improve processes.

There are more than 220 million credit-active consumers. Hear from a data expert on ways analysts can explore these files and dig into big data with ease.

Trended attributes can provide significant lift in the development of segmentation strategies and custom models are used effectively across the life cycle.

Most C-level executives (87%) believe data has greatly disrupted their organization’s operations over the past 12 months.

Reject inference design is used to classify the performance outcome of prospective customers within the declined or nonbooked population so this population’s performance reflects its performance had it been booked.

Holiday spend for 2017 was healthy, translating into big business for credit card portfolios. But how do card companies keep the business in 2018?

Knowing which of your customers may receive a tax refund is critical. Trended data can help collectors understand who will use it to pay down debt.

The phrase swap set refers to “swapping out” a set of customer accounts and replacing them with, or “swapping in,” a set of good customer accounts.

With 81% of Americans having a social media profile, you may wonder if social media insights can be used to assess credit risk. When considering social media data as it pertains to financial decisions, there are 3 key concerns to consider.