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3 Emerging Data Challenges in Credit Management (According to McKinsey)

In recent years, financial institutions have increased their adoption of data and new technologies to manage credit portfolios. In fact, McKinsey’s recent survey of financial institutions says there is significant progress in using new data and techniques for credit portfolio management. 

But it also shines a spotlight on the challenges that remain around data and technology in the financial services sector. 

In this article, we recap on McKinsey’s study, examine the top 3 data challenges for credit management, and look at some interesting ideas to overcome them. 

McKinsey’s 2022 Financial Institution Study: A quick recap

McKinsey surveyed 44 financial institutions globally on the latest developments in data and analytics for credit portfolio management. 

The objective

To understand the use of traditional and alternative data sources for credit risk information, to determine how financial institutions use analytical approaches across portfolio segments, and to inform the path forward to incorporate next-generation data and analytics.

The core findings 

Financial institutions have made significant progress in using new data for credit portfolio management:

  • 60% of respondents said they have increased their use of new types of data and deployed advanced analytics techniques in credit portfolio management. 

  • 75% expect these trends to continue over the next two years.

However, there are emerging challenges getting in the way of using new data for credit management, namely:

  • Data quality, as cited by 63% of respondents.

  • Resources, as cited by 42% of respondents.

  • Data costs, as cited by 30% of respondents. 

In summary, while progress is being made, barriers remain for financial institutions that want to improve credit portfolio management. 

With this in mind, let’s get into the details – starting with insight into the data being used. 

What types of data are financial institutions using for credit management today?

As they look to deploy new analytics within credit management, companies are obtaining data from sources like: 

  • Internal credit behaviour data and cross-product data 

  • Data from the credit bureaux

  • Economic forecasts

  • And new data from external providers. 

This includes alternative data as well; for example, in the corporate portfolio, more than half of respondents are currently using, piloting, or considering news media, social media, or third-party account data.

Our view on this is that utilising all existing internal and bureau data, which is usually in separate parts of systems and product/customer databases is one problem. Another is taking a customer database and matching it against data providers. This can be costly and time-consuming and won't add value necessarily.

The top 3 emerging data and technology challenges 

As we touched on earlier, every participant in McKinsey’s study was asked about the biggest challenges facing credit risk in the next two to three years. 

The top three emerging challenges are:

  • #1: Data quality: 60% cited data quality as the top constraint to using innovative new data sources

  • #2: Resources: 42% cited resources as the second emerging challenge.

  • #3: Data cost: 30% cited the cost of data as the third largest challenge.

Let’s look at each challenge in more detail… 

#1: Data quality

Considering that financial institutions leverage enormous amounts of data to make critical consumer decisions, they require data accuracy and integrity at all times. 

If customer data is incomplete or scoring methodologies are inaccurate, the outputs can severely affect consumer fairness. Moreover, financial services are time-sensitive where a single error quickly multiplies downstream processes. 

Improving data quality calls for increased transparency in the data held by data providers, such as the top three bureaux. 

#2: Resources

In addition to resources being flagged as a top challenge by McKinsey, it was also highlighted by more than a quarter of senior executives in the financial sector in a study by data and analytics consultancy Cynozure

The research also found that 39% felt senior executives did not fully understand the value of data. One of the key reasons for this skills gap is due to the pace of technological change. 

Likewise, data scientists, data analysts, and data engineers are all in high demand. 

Broadly speaking, there are two options when looking to plug the gap: reskilling and upskilling existing staff to provide them with better data skills; or hiring external talent. 

#3: Data costs

According to PWC, large banks around the world spend as much as $88 million a year on data - information they are obliged to make informed decisions and comply with regulations. Yet there is a distinct lack of transparency when it comes to bureau data pricing. Something we have discussed at length in previous articles.  

From our work with banks and other lenders, we know financial institutions can greatly reduce the cost involved in purchasing data. 

Banks and lenders are seeing strong results:

  • Negotiating contract mid-term saves on average 25%-40% on data costs – even when they stay with the same supplier. 

  • Using data from multiple bureau sources can help with pricing leverage, and diverse data sources – and even cover gaps in credit history that other bureaux may have.

  • One bank even reduced costs by £3 million per year making a 50% saving, with ongoing flexibility to use additional data in the customer lifecycle at no extra charge.

In summary, there is a significant opportunity for lenders to reduce their data costs and gain higher-quality data through increased transparency in data pricing and quality. 

Addressing these challenges with the right framework

McKinsey’s survey indicates that while credit portfolio managers are starting to use innovative data sources, major barriers remain. From finding the right data quality through to resources and data costs. 

McKinsey goes on to say that the evaluation of data sources, as well as increased transparency, will help financial institutions understand the evolving data and vendor landscape. And we certainly agree. 

In our view, these challenges are nothing new. This is something we see time and again through our work supporting financial institutions. 

The good news is: Financial institutions can take five steps to address the key data issues:

#1: Understand data requirements: This includes data sources, data quality, and data accuracy. By working with external specialists, you can map out existing data sources and what you’re paying.

#2: Assess data quality and pricing gaps: Compare your pricing to others with the same supplier and footprint. 

#3: Evaluate the company’s data benchmark: Look for all potential savings and discover target pricing. 

#4: Build out the waterfall of data and bureaux you should use:  More on this here

#5: Negotiate: Or refresh data contracts, policies, and procedures with support along with negotiation levers throughout each iteration of the benchmarking process.  

Address your data challenges with data benchmarking

To wrap up, the benefits of a data benchmarking approach are clear and should motivate institutions to intensify their efforts to source the most high-quality data at the right price. 

Full insight into data bureau pricing, quality, and accuracy can provide a personalised comparison to inform supplier negotiations – whether you choose to stay with your current provider, move to another, or adopt a multi-bureau approach.

If you’re interested in how data benchmarking works, leave a comment below. 

 

 

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Nick Green

Nick Green

Director

Purple Patch Broking Ltd

Member since

07 Dec 2020

Location

Stratford-Upon-Avon

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65

This post is from a series of posts in the group:

Financial Risk Management

This network brings together professionals involved in the oversight and management of their company's financial risks and exposures as well as solution vendors, in order to discuss risk issues including interest rate risk, foreign exchange risk and commodity price risk, among others.


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