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Big data and risk management


Posted By Pinar Dost ⋅ October 24, 2019

Risk, and its evaluation, is a part of life. Often, we manage everyday risk without even thinking about it, such as picking our moment to drive out of a junction or deciding to play an extra lucky dip on a rollover lottery.

When it comes to the business world though – and in particular, the finance and insurance sectors – risk is something that needs as much assessment and mitigation as possible. Today’s abundance of data and rapidly evolving analysis techniques is giving these industries more sophisticated risk management methods than ever before.

And there’s plenty of risk to manage. The financial and insurance-related markets have become more complex and interconnected over time, with a growing number of risk areas as a result. From credit risk (the probability of loss due to a borrower’s failure to make payments on any type of debt) to cyber risk (any financial loss, disruption or damage that results from a failure of information technology systems) and everything in between, it all needs overseeing. As McKinsey state, since the global financial crisis of 2008, risk functions have become much more rigorous, requiring scrupulous management of both financial and non-financial risk.

As we explore here, big data can shine a light on all types of risk management for financial services and insurers, from revealing the hidden consumer behaviours that can contribute to ultra-precise credit approvals, to the detection of financial crime.

As we explore here, big data can shine a light on all types of risk management for financial services and insurers, from revealing the hidden consumer behaviours that can contribute to ultra-precise credit approvals, to the detection of financial crime.

Live data enables lightning-fast credit approval

Traditionally, a business’s credit worthiness has always been dependent on official credit score and history, which can take time for fledgling companies to amass. Now though, big data analysis is enabling a new type of lender to weigh up a business’s credit suitability based on actual, real-time business performance, and to return with a decision in record time.

US-based small business loan platform Kabbage analyses two million live data points from across the web to assess business loan applications in just minutes. Kabbage reviews a business’s revenue activity, consistency and cash flow with a wide range of third-party online services, including Paypal, Ebay and Sage, and uses artificial intelligence (AI) to calculate an optimum loan amount. Despite only being founded in 2009, Kabbage is on track to lend between $2.4 and $3 billion in 2019.

Tiered data lakes allow for in-depth risk analysis

Investment banks thrive on their ability to manage risk and make the most lucrative financial judgements. The world’s biggest investment banks devote millions to data analysis for risk management, often developing their own technology platforms capable of providing ever-more accurate predictions.

At Morgan Stanley, an in-house ‘Detailed Subledger’ (DSL) project ensures that the bank’s analysts consider all of their key data assets in one place when assessing risk. The DSL is a single store of financial data (from trading systems, risk models, accounting systems etc) structured in a three-tier architecture – database, service layer and user interface. The middle service layer converts simple search requests at user interface level into complex coded queries that will extract all relevant information from the database, so that the bank’s analysts can quickly harvest all the data they need to perform thorough risk management, without chancing that anything could be missed.

Cloud-based data analysis improves fraud detection

Fraud is an immensely costly problem for financial services; despite the fact that security advancements successfully prevented more than £1.6 billion of unauthorised fraud in the UK last year, another £1.2 billion was lost to fraud and scams.

Like Morgan Stanley and its DSL, international bank HSBC has developed its own solution to tackle the issue of fraud detection – a Global Social Network Analytics platform. The aim of the platform is to use AI to screen vast amounts of internal, public and transactional data about its customers in order to spot potential money laundering activity. It screens not only customer data held by HSBC, but broader networks to create a holistic view of their online connections.

Don’t risk being left behind by the data revolution

Just as data and its analysis is enabling financial services and insurers to refine their methods and improve their consumer offering, it could do the same for your business. Whatever your organisation, and whether or not risk management is something you want to improve, our expert team can help you define your unique data objectives and determine how seizing the data can help you achieve them.

The first step is getting in touch to discuss your business needs, so contact us today to find out how to get started. 

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