Credit risk – Building on a foundation of quality data

Credit risk analysts at emerging market banks not only need high-quality data, but also the necessary tools to manage it. Improving consistency and reducing the risk of errors in credit risk data create more time to concentrate on the core activity of analysis

Regulatory and competitive pressures in global financial markets have combined in such a way that banks are now required to handle increasing volumes of data. Having the right tools available to gather and standardise this information to fit internal credit risk analysis systems can save time and effort, allowing the actual analytical process to start on a strong footing. 

However, for banks in emerging markets in particular, it can be a struggle to not only find the necessary information, but also build the best systems to efficiently and effectively standardise and format this data. 

“International Financial Reporting Standard (IFRS) 9 is a big challenge for banks, especially those using the standardised approach that may not have much historical data,” says a senior credit risk professional currently working at a bank in Egypt, referring to the International Accounting Standards Board’s IFRS for financial instruments. “Many banks in this region have to dig from scratch to get the data, do the corporate analysis, get the macroeconomic information, and so on.”

Paul Whitmore, global head of counterparty risk solutions at Fitch Solutions, agrees that larger, more data-driven banks – especially those operating in developed economies – typically have the expertise and resources to create their own models. They can draw on internal data warehouses for default and fundamental data, while banks in frontier regions often purchase this information from vendors or find it online. 

“Larger banks can tweak internal models to address regulations such as IFRS 9 […] whereas a lot of smaller banks use the standardised approach, so they don’t necessarily have internal models, and must create something from scratch,” he adds.

The most important task is collecting the correct information for the credit risk analysis and reporting in order to conduct the required assessment and analysis using this information correctly […] and to use it effectively in assessing the credit risk to avoid any potential loss

Fatma Sayari, wholesale credit risk analyst, Bank ABC

For most market participants, quality data forms the foundation of their credit risk analysis infrastructure. This can come from an in-house data warehouse or external sources such as company websites. Alternatively, an organisation can purchase credit assessments for specific sectors or regions from a third-party vendor that can provide a comprehensive analysis based on a variety of qualitative and quantitative criteria.

Once banks in emerging regions locate the correct information, they must begin the process of integrating it into their own internal systems before the credit risk analysis process can even begin. This often involves checking the data for accuracy before standardising and formatting it. 

“You have to check the data quality first because, at the end of the day, it’s rubbish in, rubbish out,” explains the Egypt-based credit risk manager. “So banks have to spend a lot of time cleansing the data, checking for duplicates and checking everything is correct before putting it in the models. For me, data quality is the first priority, and then the infrastructure and people.” 

Fatma Sayari, wholesale credit risk analyst at Bank ABC in Tunisia, agrees that data quality is key: “The most important task is collecting the correct information for the credit risk analysis and reporting in order to conduct the required assessment and analysis using this information correctly […] and to use it effectively in assessing the credit risk to avoid any potential loss,” she says.

Errors introduced at the credit risk analysis stage, either due to input mistakes or a faulty collection process, can reverberate around the organisation. “For credit risk analysis, we are the second line of defence and we try to communicate the most efficient information for committees with full transparency in order to take the right decision for the counterparty,” Sayari adds. 

However, there are tools available to help credit risk analysts in emerging markets access quality data, ensuring more time is spent on the key activity of analysis rather than checking and formatting. 

“Personally, I think it has become easier to obtain information [in recent years],” says Valentin Valentin Redondo, senior credit risk analyst, corporate banking at Banco Cooperativo Español. “Rating agencies offer increasingly personalised services so banks can obtain value from these tools and help us to carry out a more accurate analysis of credit risks.” 

Robust credit risk analysis requires access to quality data. Having fast, efficient tools to get that data in a usable format from reliable sources will save time for analysts and support a more accurate and consistent approach to credit risk analysis. This will not only satisfy regulators, but also enable emerging markets banks to keep up with the competition in the increasingly globalised financial markets. 

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