Alternative data and emerging technologies are two components of future lending
The consumer credit market is large and complex, but it changes rapidly. One of the biggest trends in the consumer credit market is the increased use of alternative data sources in credit decisions. The banking industry is still transitioning from traditional data collection and analysis methods to more advanced techniques such as artificial intelligence (AI), predictive analytics, and machine learning. Lending organizations continue to improve their credit decisions; we are moving into a world where more data is available, and banks need to look at all the data to make better decisions. Algorithms are making these decisions and getting more intelligent as we feed them more information from more sources. Banks have versions of how they apply an algorithm or scoring system, for that matter. However, it is about feeding machine more information by looking at different data types like social media accounts and other online activities, including mobile phone usage patterns.
Lending organizations continue to strive to improve their credit decisions
Today, lending organizations continue to strive to improve their credit decisions. They are looking beyond traditional data sources and are looking at alternative data sources.
Organizations are also interested in the volume, velocity, and variety of data they can access via alternative sources and how they can use that information to make better credit decisions.
We are moving into a world where more data is available, and banks need to look at all of the data to make better decisions
The credit decision process is becoming increasingly complex with the emergence of alternative data. Banks must look at their available data, including alternative sources such as social media, when making credit decisions.
The volume, velocity, and variety of data have changed tremendously over the past few years:
• Data is everywhere
• Data is growing exponentially
• Data is being generated by many different sources
• Data is being generated by more people
• Data is being generated more quickly and in a more diverse way
Algorithms are making these decisions and getting more innovative as we feed them more information from more sources
Algorithms are making these decisions and getting smarter as we feed them more information from more sources. We can easily see the power of this approach when looking at machine learning, which is used in many credit decision models today. One study found that machine learning can predict delinquency with 90% accuracy after 14 months of data collection -- meaning that it's possible to make predictions without having to wait through an entire credit cycle.
In addition, technological innovations mean that big data analytics are now being applied not only to traditional financial data but also to social media posts, online reviews, and even mobile phone usage patterns. For example, some lenders are using social media posts to predict whether someone will default on their loan. Others have started using algorithms based on text analysis software to determine whether an applicant might be too risky (which could lead them down an "algorithm rabbit hole" with no end).
Banks have their versions of how they apply the algorithm or scoring system, for that matter, but it is about feeding the machine more information:
- • Banks have their versions of how they apply the algorithm or scoring system, for that matter, but it is about feeding the machine more information.
- • The more data you provide to a machine, the brighter it becomes and the better decision it will make.
What constitutes a meaningful set of alternative data will vary from one lender to another, but it is clear that lenders have already begun viewing alternative data as a source of significant information in addition to traditional data sources traditionally used in credit decisioning
Alternative data is becoming increasingly crucial in credit decisions, especially given the potential to improve risk assessment and reduce adverse selection. In this post, we'll look at what constitutes "meaningful" sets of alternative data and how different types of alternative data can be used to predict future behavior that may translate into an increased propensity for default.
We will also discuss new machine learning approaches and advanced algorithms that enable lenders to make better underwriting decisions by building models on alternative data sources such as social media feeds or purchase history at online retailers.
The future of credit decisions will require banks to use alternative data as a source of meaningful information in addition to traditional data sources traditionally used in credit decisions.