PD is a measure of credit rating that is assigned internally to a customer or a contract with the aim of estimating the probability of non-compliance within a year. The PD is obtained through a process using scoring and rating tools.
This tool is a statistical instrument focused on estimating the probability of default according to features of the contract-customer binomial. They are focused on management of retail credit: consumer, mortgages, credit cards of individuals, corporate loans, etc. There are different types of scoring: reactive, behavioral, proactive and bureau.
The main aim of reactive scoring is to forecast the credit quality of loan applications submitted by customers. It attempts to predict the applicant’s probability of default if the application were accepted (as they may not be BBVA customers at the time of application).
The level of sophistication of the scoring model and its capacity to adapt to the economic context enables it to give more accurate customer profiles and improve the Bank’s capacity to identify different levels of creditworthiness within specific groups (young people, customers, etc.). The result is a significant improvement in the discrimination capacity of tools in groups of particular interest to the business.
The accompanying charts 5 and 6 show default rates of some of the reactive scoring tools used by the Group.
They show how different axes can serve to assess the risk of a retail-type operation. Chart 5 shows the various default rates by seasoning and score groups, within one year, of BBVA Spain mortgages for the domestic customer segment. Chart 6 presents the different default rates for transactions granted for a period of under 6 years and those for 6 years or more in BBVA Spain Autos Finanzia. As expected, the highest probabilities of default are observed for transactions granted for a longer term.
A distinguishing feature of reactive scorings is that the default rates of the various segments tend to converge over time. In BBVA, this loss of screening capacity is mitigated by combining reactive with behavioral and proactive scorings.
Behavioral scoring is used to review contracts that have already been formalized by incorporating information on customer behavior and on the contract itself. Unlike reactive scoring, it is an a posteriori analysis, i.e. once the contract has come into force. It is used to review credit card limits, monitor risk, etc., and takes into account variables directly linked to the operation and the customer that are available internally: the behavior of a particular product in the past (delays in payments, default, etc.) and the customer’s general behavior with the Entity (average balance on accounts, direct debit bills, etc.).
Proactive scoring tools take into account the same variables as behavioral scorings, but they have a different purpose, as they provide an overall ranking of the customer, rather than of a specific operation. This customer perspective is supplemented by adjustments that depend on the product type. The available proactive scorings has enabled the Group to monitor customers’ credit risk more precisely, to improve risk screening processes and to manage the portfolio more actively (i.e. by offering credit facilities adapted to each customer’s risk profile).
Chart 7 presents the proactive scoring default probability curves in consumer finance for individual customers closely-linked to BBVA Spain. On the other hand, chart 8 depicts the behavioral scoring for BBVA Bancomer credit cards.
The so-called bureau scoring models, widely used in The Americas, are also of great importance. This kind of tool is similar to the scorings explained above, except that while the latter are based on the Bank’s internal information, bureau scoring requires credit information from other credit institutions or banks (on default events or customer behavior). In those countries with positive bureau information, external and internal information are combined. This information is provided by specialized agencies that compile data from other entities. Not all banks collaborate in supplying this information, and usually only participating entities have access to it. In Spain, the Bank of Spain’s Risk Information Center (CIRBE) makes such information available. Bureau scorings are used for the same purpose as the other scorings, i.e. authorizing operations, setting risk limits and monitoring risk.
An adequate management of the reactive, behavioral, proactive and bureau tools by the Group allows to gather updated risk parameters adapted to economic reality. This results in precise knowledge of the credit healthiness of operations and/or customers. This task is particularly relevant in the current economic situation, as it permits identifying the contracts and customers that are in difficulties, and thus taking the necessary measures to manage risks that have already been assumed.
These tools focus on wholesale customers: companies, corporations, SMEs, the public sector, etc. In such cases, default events are predicted at the customer level rather than at the contract level.
The risk assumed by BBVA in the wholesale portfolios is classified in a standardized way by using a single master scale for the whole Group that is available in two versions: a reduced one with 17 degrees; and an extended one, with 34. The master scale aims to discriminate amongst credit quality levels, taking into account geographical diversity and the different risk levels in the different wholesale portfolios in the countries where the Group operates.
The information provided by the rating tools is used when deciding on accepting operations and reviewing limits.
Some of the wholesale portfolios managed by BBVA are low default portfolios, in which the number of default events is low (sovereign risks, corporations, etc.). To obtain PD estimates in these portfolios the internal information is supplemented by external information, mainly from external rating agencies and the databases of external suppliers.
As an example, below is the rating tool’s parametric curve for defaults of BBVA Argentina client companies by internal score assigned (Chart 9).
The economic cycle in PD
The current economic crisis has revealed the importance of anticipating events in risk management. In this context, excess cyclicality of risk measurements has been identified as one of the causes of the instability of the metrics of financial institutions. BBVA has always been committed to estimating average cycle parameters that mitigate the effects of economic-financial turbulence in credit risk measurement.
The probability of default varies according to the cycle: it is greater during recessions and lower during expansions. The adjustment process to translate the default rates observed empirically into average default rates is known as cycle adjustment. The cycle adjustment uses sufficiently long economic series related to the default of portfolios, and their behavior is compared with that of the default events in the Entity’s portfolios. Any differences between past and future economic cycles may also be taken into account, thus resulting in a certain prospective approach.
Chart 10 illustrates how the cycle adjustment mechanism works. It shows the hypothetical evolution of a series of default events over more than one economic cycle. The cycle adjustment model used by BBVA extrapolates the performance of this series of default events to internal data, based on the relationship between the series over one entire cycle and the observation period.