{"id":2271,"date":"2015-11-20T18:46:17","date_gmt":"2015-11-20T23:46:17","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/transunion-competing-with-data-giants\/"},"modified":"2015-11-20T18:46:17","modified_gmt":"2015-11-20T23:46:17","slug":"transunion-competing-with-data-giants","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/transunion-competing-with-data-giants\/","title":{"rendered":"Transunion – Competing with Data Giants"},"content":{"rendered":"

Transunion describes itself as \u201ca leading global risk and information solutions provider to businesses and consumers.\u201d\u00a0 Most of us know it as one of the 3 primary credit bureaus that monitor our creditworthiness.\u00a0 About \u00be of Transunion\u2019s revenue comes from businesses evaluating consumers\u2019 ability to pay, managing credit risk, investigating fraud, etc.\u00a0 The remaining \u00bc of revenue comes from consumers who subscribe to Transunion to track their credit profile and score.<\/p>\n

As one might expect for a company that traffics in consumer information, Transunion is one of the most data-intensive companies in the world.\u00a0 In fact, when it went public earlier this year, Transunion dedicated the front page of its prospectus to listing different measures of its data usage:<\/p>\n

\"Picture<\/a><\/p>\n

These numbers are continuously increasing.\u00a0 In fact, the number of petabytes of data that Transunion maintains has grown by 25% per year since 2010.\u00a0 Transunion sifts through this massive amount of data by employing machine learning techniques with sophisticated algorithms.<\/p>\n

Transunion believes it has an attractive market opportunity.\u00a0 Indeed, it defines its opportunity as the $59 billion spent on \u201cbusiness analytics services\u201d each year.\u00a0 As IDC notes, this market is rapidly growing at ~15% per year:<\/p>\n

\"Picture<\/a><\/p>\n

However, while Transunion is undoubtedly a \u201cbig data\u201d company playing in an attractive space, it\u2019s important to evaluate it in the context of other big data companies.\u00a0 After all, its current revenue is only ~$1.5 billion of the purported $59 billion market and is small even when compared to the subset of other major credit bureaus \u2013 Equifax generates ~$2.7 billion while Experian is at ~$4.6 billion.\u00a0 This might appear to be negative for Transunion because credit bureaus exhibit direct network effects.\u00a0 The more data a credit bureau already has, the more useful its analysis will be for customers.\u00a0 And the more customers that use a credit bureau, the more data it will acquire \u2013 this is because customers provide data in addition to consuming it (i.e. banks offer the credit bureaus information on consumer defaults in addition to purchasing data from credit bureaus when underwriting consumer loans).\u00a0 Transunion survives as a smaller player because it has convinced enough customers to purchase its credit analytics instead of or in addition to that of Experian and Equifax \u2013 this has prevented the market from devolving to a \u201cwinner-take-all\u201d situation (though it has perhaps become one where \u201cthree winners take all\u201d).\u00a0 Transunion has accomplished this by acquiring unique data sources and developing unique analytics.\u00a0 For instance:<\/p>\n

(1) Time Series Data Analysis<\/u><\/em><\/p>\n

Most credit analyses evaluate a consumer\u2019s creditworthiness at a given point in time.\u00a0 Transunion was the first bureau to recognize that time series data could be an effective supplement to point-in-time data in predicting default risk.\u00a0 For instance, a consumer who has been paying down debt could be lower-risk than a consumer who has been building debt, but a traditional credit score would treat them the same if they have the same current level of debt:<\/p>\n

\"Picture<\/a><\/p>\n

Transunion \u201cproductized\u201d this insight with CreditVision<\/em>, the first solution from any of the credit bureaus to leverage trended data to help customers make lending and marketing decisions.\u00a0 It did so by developing an algorithm that analyzed up to 30 months of historical data covering \u201cchanges in balances, credit limits, past due amounts, and payments.\u201d\u00a0 This resulted in scores that ended up being superior predictors of default.<\/p>\n

(2) Blending Public Records Data with Credit Data<\/u><\/em><\/p>\n

Customers typically have to purchase public records information separately from credit data.\u00a0 For instance, Bank of America might pay LexisNexis to authenticate a consumer\u2019s identity \/ arrest history and might pay Equifax for the same consumer\u2019s debt default history before underwriting a mortgage.\u00a0 However, not only is it cumbersome to procure information from multiple vendors, but Bank of America must rely on its internal resources to determine how to integrate the public records and credit data in making a final assessment.<\/p>\n

Transunion, whose CEO formerly led LexisNexis, recognized this pain point and led a multiyear effort to acquire public records datasets and to associate them on a person-by-person basis with credit histories.\u00a0 As a result, Transunion is now the only scale provider with both public records and credit data.\u00a0 It productized this solution as TLOxp<\/em>, which \u201cleverages data matching capabilities across various datasets to identify and investigate relationships between people, assets, locations, and businesses, allowing us to offer enhanced due diligence, threat assessment, identity authentication, and fraud prevention and detection solutions.\u201d<\/p>\n

At this point, the blended datasets effectively represent a proprietary database.\u00a0 The barrier to entry is not only the upfront time and effort Transunion invested to collect and integrate the data, but also the expanded data flow the company now receives because it offers TLOxp<\/em>.<\/p>\n

(3) Industry-Specific Data<\/u><\/em><\/p>\n

In addition to integrating historical data and blending datasets, Transunion has tried to differentiate itself by acquiring industry-specific data to complement baseline consumer credit data.\u00a0 For instance, Transunion has obtained vehicle-level data (i.e. accident history) to help auto insurers underwrite policies more efficiently.\u00a0 In healthcare, Transunion developed a product to help insurers search for alternate plans under which a given consumer may be covered (i.e. Medicaid, Supplemental Security Income, TRICARE) to minimize claims payouts.<\/p>\n

While Experian and Equifax have vertical solutions of their own, Transunion\u2019s industry-specific data and analytics helps it serve customers more effectively in the industries it chooses to play in.<\/p>\n

(4) Building Business in New Geographies<\/u><\/em><\/p>\n

Recognizing its limitations as a smaller player in developed economies, Transunion has aggressively built out its international business.\u00a0 For instance, Transunion co-founded (along with a local partner) the first credit bureau in India in 2001 and has benefitted not only from the country\u2019s rapid GDP growth, but from the even faster growth of the credit-active population.\u00a0 Being first to market is especially valuable in countries like India where datasets are not as readily available for purchase \u2013 although it takes longer to achieve scale, the barriers to entry are higher because the first player accumulates a more proprietary set of data.\u00a0 It can therefore offer higher-quality data\/analytics at lower marginal cost than new entrants.<\/p>\n

Transunion is attempting to replicate its Indian success in other geographies \u2013 international revenues now make up almost 20% of its business.<\/p>\n

Challenges<\/u><\/em><\/p>\n

While Transunion has been scrappy in differentiating itself versus its larger peers to date, it faces a number of challenges going forward.\u00a0 First and most importantly, unique analytics can be replicated if the underlying data is not proprietary.\u00a0 For instance, the historical data in CreditVision<\/em> is not as unique as it was when it was first introduced a few years ago because Equifax and Experian have recently developed similar solutions (Equifax introduced the Dimensions<\/em> product suite while Experian put out its Trended Solutions<\/em> product).<\/p>\n

Even seemingly proprietary data, such as Transunion\u2019s blended public records\/credit data, can be replicated over time. \u00a0While the company does now receive incremental data due to its blended solution, it only took a few years for it to build the blended database in the first place.<\/p>\n

Industry-specific data, while interesting, puts Transunion into competition with niche vendors who may have superior vertical solutions.\u00a0 For instance, Solera and Polk are dominant competitors in the vehicle history \/ auto insurance space.\u00a0 Indeed, Transunion procures much of its auto data from vendors like these and is therefore basically a reseller of this data.<\/p>\n

The international opportunity, while interesting, is nascent enough that the market may not have \u201ctipped\u201d to the point where new competitors cannot enter.\u00a0 For instance, even after massive growth over the last 15 years, only 15% of India\u2019s adult population is \u201ccredit active.\u201d<\/p>\n

While it\u2019s difficult to know the ease with which competitors can replicate Transunion\u2019s differentiators, it is clear that the company will have to continue innovating just to stay in place.<\/p>\n","protected":false},"excerpt":{"rendered":"

How Transunion remains relevant as the #3 credit bureau<\/p>\n","protected":false},"author":146,"featured_media":2272,"comment_status":"open","ping_status":"closed","template":"","categories":[882,884,883],"class_list":["post-2271","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-creditbureau","category-petabytes","category-stayingalive"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-driven-value-creation-value-capture-and-operating-models\/","yoast_head":"\nTransunion - Competing with Data Giants - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/transunion-competing-with-data-giants\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Transunion - 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