Leveraging Big Data to Revolutionize Mortgage Banking..

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“Perhaps more than anything else, failure to recognize the precariousness and fickleness of confidence-especially in cases in which large short-term debts need to be rolled over continuously-is the key factor that gives rise to the this-time-is-different syndrome.Highly indebted governments, banks, or corporations can seem to be merrily rolling along for an extended period, when bang!-confidence collapses, lenders disappear, and a crisis hits.”   – This Time is Different (Carmen M. Reinhart and Kenneth Rogoff)

Tomes have been written about the financial crisis of 2008 (GFC  -as it’s affectionately called in financial circles). Whatever be the mechanics of the financial instruments that caused the meltdown –  one thing everyone broadly agrees on was that easy standards with respect to granting credit (and specifically consumer mortgages in the US with the goal of securitizing & reselling them in tranches – the infamous CDO’s) were the main causes of the crisis.

Banks essentially granted easy mortgages (in part to huge numbers of high risk, unqualified customers) with the goal of securitizing these, marketing and selling them into the financial markets by dressing them as low risk & high return investments.  AIG Insurance’s financial products (FP) division created & marketed another complex instrument – credit default swaps – which effectively insured the buyer from losses in the case any of the original derivative instruments made a loss.

For a few years, the Mortgage Market had largely been transformed into a risk averse operation however rebounding in recent times with the economic recovery. Higher loan production efficiencies and  favorable hedging outcomes on hedges helped drive an increase in mortgage banking profits during the second quarter of 2015.

The Mortgage Bankers Association reported that average net pretax income jumped 55.7 percent from the first quarter to $3.50 million in the second. That was the best pretax income figure since the first quarter of 2013. (Source – Inside Mortgage Banking).

However internet based lenders and new Age FinTechs are encroaching on this established space by creating agile applications that are internet enabled by default & Digital by Design across the front, back and mid offices. These services vastly ease the loan application & qualification  processes (sometimes processing loans in a day as compared to the weeks with traditional lenders), while offering a surfeit of other integrated services like financial planning & advisory, online brokerage and bill payments etc. All of these services are primarily underpinned on advanced data analytics that provide – 1) a seamless Single View of Customer (SVC) as well as 2) advanced Digital Marketing capabilities that can capture a 3) Customer Journey across a slew of financial products.

However there is a significant need for existing players to be able to gain such efficiencies that are missing in their IT capabilities due to antiquated technology & data architectures. It is no longer possible for them to remain profitable in the coming years unless innovation is adopted at the core of their IT infrastructures.

If Mortgage lenders are to take a Big Data approach augmenting complementary investments in other Digital technology – Mobile, Web Scale, DevOps, Automation and Cloud Computing – then what are the highest value business use-cases to apply this to?

Big Data can be applied to the Mortgage Market business spanning six broad buckets as delineated below –

1. Account Origination & Underwriting – Qualifying borrowers for Mortgages based on not just historical data that is used as part of the origination & underwriting process (credit reports, employment & income history etc) but also data that was not mined hitherto (social media data, financial purchasing patterns,). It is a well known fact there are huge segments of the population (especially the Millenial’s) who are broadly eligible but under banked as they do not satisfy some of the classical business rules needed to obtain approvals on mortgages
2. Account Servicing –Servicing is a highly commodified, low margin, high transaction volume business, and it serves an industry that has shrunk over 12% since 2008 (from $11.3 trillion to $9.9 trillion) – Ref Todd Fischer in National Mortgage News. Innovation here will largely be driven by players who  apply sophisticated analytics to make better-informed decisions that will result in enhanced risk mitigation, improved loan quality, higher per transaction margin, and increased profitability. Also, combining real time consumer data (household spending, credit card usage, income changes) with historical data to assess eligibility for either approvals in Home Equity Lines of Credit (HELOC) or an increases in mortgage borrowing. Predicting when a young family will want to move out of a starter home to a larger home based on data such as childbirth etc. On the flip side, being able to detect patterns that could indicate financial distress & subsequent delinquency (based on macro indicators like large numbers of defaults in a specific county or micro indicators like loss or break in employment) on the part of borrowers – across a range of timelines is an excellent example of this capability.
3. Cross Product Selling – Mortgages have historically been a highly sticky financial product that entails a Bank-Customer relationship spanning 10+ years. Such considerable timelines ensure that Banks can build relationships with a customer than enables them to sell bundled products like Auto Loans, Private Banking Services, Credit Cards, Student and Consumer loans over the lifespan of the account. Underpinning all of this are the rich troves of data that pertain to customer transactions & demographic information.
4. Risk & Regulatory Reporting –  Post the financial crisis, the US Government via the Federal Housing Administration (FHA) has put in place a stringent regulatory mandate with a series of housing loan programs that aim to protect the consumer against predatory lending. These range from FHA- HARP to FHA-HAMP to the Short Refinance Program to HEAP to the FHA-HAPA. Banks need to understand their existing customer data to predict and modify mortgages as appropriate for borrowers in financial distress. Predictive modeling using Big Data techniques is a huge help in this analysis.
5. Fraud Detection – Mortgage fraud is a huge economic challenge and spans areas like foreclosure fraud, subprime fraud, property valuation fraud etc.  Law enforcement organizations including the FBI are constantly developing and fine-tuning  new techniques to analyze, detect and combat mortgage fraud. A large portion of this to collect and analyze data to spot emerging trends and patterns. And we are using the full array of investigative techniques to find and stop criminals before the fact, rather than after the damage has been done.
6. Business Actions –  One of the facts of life in the fast moving mortgage market are business actions ranging from whole sale acquisitions of lenders to selling tranches of loans for sub-servicing. The ability to analyze a vast amount of data (ranging in Petabytes) with multiple structures to determine an acquisition target’s risk profile, portfolio worthiness are key to due diligence. The lack of such diligence has led to (famously) suboptimal acquisitions (e.g. BofA – Countrywide & JP Morgan – Washington Mutual to name a couple). These in turn have led to executive churn,  negative press, massive destruction of shareholder value & the distraction of multiple lawsuits.

How can Big Data Help? 

Existing data architectures in the mortgage sector are largely silo-ed with IT creating or replicating data marts or warehouses to feed internal lines of business. These data marts are then accessed by custom reporting applications thus replicating/copying data many times over which leads to massive data management headaches & governance challenges.

Furthermore, the explosion of new types of data (e.g Social Media, Clickstream,housing price indices,demographic migration data etc) in recent years has put tremendous pressure on the financial services datacenter, both technically and financially, and an architectural shift is underway in which multiple LOBs can consolidate their data into a unified data lake.

It is also interesting that the industry is moving to an approach of integration & augmentation given that all of the leading databases, ETL products and BI tools provide robust and certified Hadoop plugins. Hadoop can thus integrate all cross company data (mortgages, clickstreams, transaction data, payment data, account data etc) to create one scalable and low cost data repository that can be mined differently based on differing line of business requirements.

The Financial Services Data Lake (as shown below) supports multiple access methods (batch, real-time, streaming, in-memory, etc.) to a common data set which is the unified repository of all financial data, it also enables users to transform and view data in multiple ways (across various schemas) and deploy closed-loop analytics applications that bring time-to-insight closer to real time than ever before.

Finally, a Hadoop cluster with tools like Hadoop, Storm, and Spark is not limited to one purpose, like older dedicated-computing platforms. The same cluster you use for running Risk models tests can also be used for text mining, predictive analytics, compliance, fraud detection, customer sentiment analysis, and many many other purposes. This is a key point, once you can bring in siloe-d data into a data lake, it is available to running multiple business scenarios – limited only by the overall business scope.

Analysts and Data Scientists can use a variety of tools to glean insights, build & backtest models. Plenty of organizations already have purchased BI tools like Tableau, Spotfire or Qlikview. Data Scientists can use platforms like SAS or R. A series of MapReduce jobs (potentially submitted from Hive, Pig, or Oozie) are typically run to ingest, transform encode, sort, the data sets from HDFS into R. Data analysts can then perform complex modeling exercises such as linear or logistic regression,  directly on the cleansed & enriched data in R

To conclude – we are still in the early days of understanding how Big Data can impact the mortgage business. Over-regulating data management & architecture, discouraging innovation among data & business teams, as a result of an overly conservative approach or long budget cycles, is a recipe for suboptimal business results.