Tag: machine learning

Collections strategies demand diverse approaches, which is where collections analytics and collections models come into play. Read more!

The next recession is a matter of when, not if. As the economy shifts, so will the priorities for your portfolio, so now is the time to strategize.

You’ve Got Mail! Probably a lot of it. Birthday cards from Mom, a graduation announcement from your third cousin’s kid whose name you can’t remember and a postcard from your dentist reminding you you’re overdue for a cleaning. Adding to your pile, are the nearly 850 pieces of unsolicited mail Americans receive annually, according to Reader’s Digest. Many of these are pre-approval offers or invitations to apply for credit cards or personal loans. While many of these offers are getting to the right mailbox, they’re hitting a changing consumer at the wrong time. The digital revolution, along with the proliferation and availability of technology, has empowered consumers. They now not only have access to an abundance of choices but also a litany of new tools and channels, which results in them making faster, sometimes subconscious, decisions. Three Months Too Late The need to consistently stay in front of customers and prospects with the right message at the right time has caused a shortening of campaign cycles across industries. However, for some financial institutions, the customer acquisition process can take up to 120 days! While this timeframe is extreme, customer prospecting can still take around 45-60 days for most financial institutions and includes: Bureau processing: Regularly takes 10-15 days depending on the number of data sources and each time they are requested from a bureau. Data aggregation: Typically takes anywhere from 20-30 days. Targeting and selection: Generally, takes two to five days. Processing and campaign deployment: Usually takes anywhere from three days, if the firm handles it internally, or up to 10 days if an outside company handles the mailing. A Better Way That means for many firms, the data their customer acquisition campaigns are based off is at least 60 days old. Often, they are now dealing with a completely different consumer. With new card originations up 20% year-over-year in 2019 alone, it’s likely they’ve moved on, perhaps to one of your competitors. It’s time financial institutions make the move to a more modern form of prospecting and targeting that leverages the power of cloud technology, machine learning and artificial intelligence to accelerate and improve the marketing process. Financial marketing systems of the future will allow for advanced segmentation and targeting, dynamic campaign design and immediate deployment all based on the freshest data (no more than 24-48 hours old). These systems will allow firms to do ongoing analytics and modeling so their campaign testing and learning results can immediately influence next cycle decisions. Your customers are changing, isn’t it time the way you market to them changes as well?

Earlier this month, Experian joined FinovateSpring in San Francisco, CA to demonstrate innovations impacting financial health to over 1,000 attendees. The Finovate conference promotes real-world solutions while highlighting short-form demos and key insights from thought-leaders on digital lending, banking, payments, artificial intelligence and the customer experience. With more than 100 million Americans lacking fair access to credit, it's more important than ever for companies to work to improve the financial health of consumers. In addition to the show's abundance of fintech-centered content, Experian hosted an exclusive, cutting-edge breakout series demonstrating innovations that are positively impacting the financial health of consumers across the nation. Finovate Day One Overview While fintechs, banks, venture capitalist, entrepreneurs and industry analysts ascended on the general conference floor for a fast-paced day of demos, a select subset gathered for a luncheon presented by Experian North America CEO, Craig Boundy, and Group President, Alex Lintner. Attendees were given an in-depth look at new, alternative credit data streams and tools that are helping to increase financial access. Demos included: Experian Boost™: a free, groundbreaking online platform that allows consumers to instantly boost their credit scores by adding telecommunications and utility bill payments to their credit file. More than half a million consumers have leveraged Experian Boost, increasing their score by an average of 13 points. Cumulatively, Experian Boost has helped add more than 2.8 million points to consumers’ credit scores. Ascend Analytical Sandbox™: A first-of-its-kind data and analytics platform that gives companies instant access to more than 17 years of depersonalized credit data on more than 220 million U.S. consumers. It has been the most successful product launch in Experian’s history and recently earned the title of “Best Overall Analytics Platform” at this year’s Fintech Breakthrough Awards. Alternative Credit Data: Comprised of data from alternative credit sources, this data helps lenders make smarter and more informed lending decisions. Additionally, Experian’s Clear Data Platform is next-level credit data that adds supplemental FCRA-compliant credit data to enrich decisions across the entire credit spectrum. This new platform features alternative credit data, rental data, public records, consumer-permissioned data and more Upon conclusion of the luncheon, Alpa Lally, Experian’s Vice President of Data Business at Consumer Information Services, was interviewed for the HousingWire Podcast with Jacob Gaffney, HousingWire Editor in Chief, to discuss how new forms of data streams are helping improve consumers’ access to credit by giving lenders a clearer picture of their creditworthiness and risk. “Alternative credit data is different than traditional credit data and helps us paint a fuller picture of the consumer in terms of their ability to pay, willingness to pay and stability. It helps consumers get better access overall to the credit they deserve so that they can actively participate in the economy,” said Lally. Finovate Day Two Overview On the last day of the conference, expert speakers took to the main stage to analyze the latest fintech trends, opportunities and challenges. Alex Lintner and Sandeep Bhandari, Chief Strategy Officer and Chief Risk Officer at Affirm, participated in a fireside chat titled “Improving the Financial Health of America’s 100 Million Credit Underserved Consumers.” Moderated by David Penn, Finovate Analyst, the session explored the latest innovations, trends and technologies – from machine learning to alternative data – that are making a difference in positively impacting the financial health of Americans and expanding financial opportunities for underserved consumers. The panel discussed the efforts made to put financial health at the center of their business and the impact it’s had on their organizations. Following the fireside chat, Experian hosted a second lunch briefing, presented by Vijay Mehta, Chief Innovation Officer, and Greg Wright, EVP Chief Product Officer. The lunch included exclusive table discussions and open conversations to help attendees leave with a better understanding of the importance of prioritizing financial health to build trust, reach new customers and ultimately grow their business. "We are actively seeking out unresolved problems and creating products and technologies that will help transform the way businesses operate and consumers thrive in our society. But we know we can't do it alone," Experian North American CEO, Craig Boundy said in a recent blog post on Experian's fintech partnerships and Finovate participation. "That's why over the last year, we have built out an entire time of account executives and other support staff that are fully dedicated to developing and supporting partnerships with leading fintech companies. We've made significant strides that will help us pave the way for the next generation of lending while improving the financial health of more people around the world." For more information on how Experian is partnering with fintechs, visit experian.com/fintech or read our recent blog article on consumer-permissioned data for an in-depth discussion on Experian BoostTM.

To win with ML, the team and process are more important than the algorithm. Best practices before, during and after modeling to help you succeed.

The best online experience balances security and convenience. Technology and innovation is allowing businesses to give the maximum potential of both.

To satisfy technology-driven customer expectations, financial services must become familiar with the latest innovations like AI, machine learning, and APIs.

Credit access for the masses, machine learning and fraud are among the top 5 trending topics for the financial services industry in 2019.

Any analytical environment is only as good as the data you put into it. Check these four key features when choosing the right one for your organization.

At Experian, for machine learning, we use Extreme Gradient Boosting (XGBoost) implementation of Gradient Boosting Machines.

Machine learning's ability to consume vast amounts of data to uncover patterns and deliver results makes it well suited for the credit risk industry

Consumers swipe their credit cards at staggering rates, but drilling into how each individual uses their card can reveal telling details about their lifestyle and spend.

Risk analysts are insatiable consumers of big data who require better intelligence to develop market insights, evaluate risk and confirm business strategies. While every credit decision, risk assessment model or marketing forecast improves when it is based on better, faster and more current data, leveraging large data sets can be challenging and unproductive. That’s why Experian added a new functionality to its Analytical Sandbox, giving clients the flexibility they need to analyze big data efficiently. Experian’s Analytical Sandbox now utilizes H2O –an open source machine learning and deep learning platform that can model and predict with high accuracy billions of rows of high-dimensional data from multiple sources in various formats. Through machine learning and advanced predictive modeling, the platform enables Experian to better provide on-demand data insights that empowers analysts with high-quality intelligence to inform regional trends, provide consumer transactional insight or expose marketing opportunities. As a hosted service, Sandbox is offered as a plug-and-play, meaning no internal development is required. Clients can instantly access the data through a secure Web interface on their desktop, giving users access to powerful artificial and business intelligence tools from their own familiar applications. No special training is required. “AI monetizes data,” said SriSatish Ambati, CEO of H2O.ai. “Our partnership with Experian democratizes and delivers AI to the wider community of financial and risk analysts. Experian's analytics sandbox can now model and predict with high accuracy billions of rows of high-dimensional data in mere seconds.” Through H2O and the Experian Sandbox, machine learning and predictive analytics are giving risk managers from financial institutions of all sizes the ability to incorporate machine learning models into their own big data processing systems.

Newest technology doesn’t mean best when it comes to stopping fraud