Big data is… big

Alastair LuffBig Data is now a buzz word for financial services. But, put in laymen terms it simply means businesses are faced with a mass of data that is so big, it is creating confusion and a clear strategy is hard to define.

Data is generated every second and new data is adding to the complexity of it. From traditional to new data sources, the veracity and volume is growing significantly. This surge in growth presents a challenge for businesses. But, data on its own has no intrinsic value. It is the insight it provides that gives value and data can be an organisation’s best asset. The value comes from the insight it derives. An enhanced level of insight can mean generating the best possible outcomes for the business, and most importantly – the customer.

How can data confuse, or complement credit strategy?

Data can mystify if it isn’t understood. It needs to be controlled and used to avoid hindering compliance, and to generate any real value. It can also confuse the customer – with less than 8% of customers understanding how their data is being used within organisations.

Data can also complement. Organisations are not only faced with new external data sources, but over time generate their own internal data too. However, having two streams of data doesn’t mean it completes a customer profile – and in some cases, when the data has been captured over a period of time it may have become out-dated. As such, overlaying current and validated data, such as credit bureau data, can add a layer of insight that fills gaps, accredits data validity and creates an overall picture of the individual.

This comprehensive view can enhance and support lenders’ credit risk policies – ensuring a financially inclusive lending strategy that considers all relevant data assets. For example, within credit scoring.


What’s the score with the customer?

What is likely to be your top business priority over the next year?Credit scoring has been common practice for many years now. This isn’t limited to banks and lenders either. Other industries realise its benefits and scoring is offering enhanced outcomes for customer engagement and enhanced credit risk provisioning. For example, in Africa, data from mobile phone usage is helping us with credit scoring where no financial services data exists, which is giving more people access to credit.

Scores have been around for a long time. The process behind scoring has evolved over time and
organisations, specifically lenders, approach scoring differently, considering individual risk strategies, profiling and in some instances different data assets to formulate a score. All of these factors, whether standard or bespoke, can provide an automated risk assessment that identifies the credit strategy of an individual.

Complex data, made simple

Your ability to make responsible lending decisions comes down to how well you can understand the data and interpret each customer’s individual circumstances.

This is where scorecards come in to their full potential. They can help rationalise complex data and automate decision-making. Businesses who overlay internal insight into scoring, with enriched external insight such as credit bureau data will get a more comprehensive view of each customer’s credit history enabling a better credit risk assessment. This can support with consistency and confidence which can directly influence and enhance credit risk strategies. More importantly deal with individuals responsibly and fairly.

In an era confused by a mass of data, a more demanding customer and more pressure on reducing losses, businesses need to understand the value and opportunity – but balance both. This extends beyond scoring as an action, and therefore it would be prudent businesses automate this area – using available insight, to free up resource to support developments across other business areas which aren’t so easily resolved.How can scorecards enhance business efficiencies?

Using comprehensive scoring can provide advanced data feeds that contain varying benefits for the organisation, for example:

  1. Understanding affordability. What does the future financial health of an individual look like? Are they likely to experience problems? New debt-to-income ratios can provide a view of estimated incomes and in addition, organisations can validate a customer’s declared income for accuracy.
  2. Geographical insight. Some people have little or no bureau data. Using geographical analysis can provide a view of how the region and area is trending to support any credit review.
  3. Considering circumstance. Data for those with limited data, for example people living at home with their parents, can have their profile enhanced by overlaying relevant data. In addition lenders can consider the financial status of an individual, or an associate who they are linked to financially. This can provide a rounded view of any associations and identify any causes for concern.
  4. Ensuring the person is genuine. Fraud is on the rise and using data to assess and identify the genuine intent of a person can be critical to losses and also protect customers.
  5. Understanding how a person behaves. Behavioural data can provide rich insight into a person’s financial behaviours. From cash advances on credit, to limit vs. spend assessments. This can be particularly helpful in understanding a person’s financial trends and providing a prediction into their probable future trends.

Differing and advanced data assets can be used to on-board, and when a customer is on-boarded. It can be particularly useful during the lifetime of a loan in order to understand better any potential alignment to a business’s growth strategy. For example, should you increase their credit limit? Or decrease? Are they likely to need another product, or are there any concerning trends that could be suspicious?

In a world of Big Data, organisations have the opportunity to translate data into a currency. Understanding what insight it can bring, embedding it within credit risk and scoring policies can ensure accurate assessments and appropriate lending. Businesses just need to understand what data provides what – and why. This can then support and enhance your understanding which in turn can be translated into valuable insight – which will lead to growth.