4.5 Information on the IRB method

4.5.1. General information

4.5.1.1. Authorization by the Bank of Spain for the use of the IRB method

The following is a list of the models authorized by the Bank of Spain for the purpose of their use in the calculation of capital requirements.

TABLE 32: Models authorized by the Bank of Spain for the purpose of their use in the calculation of capital requirements

Institution	Portfolio
BBVA S.A.*	Financial institutions
	Public institutions
	Specialized finance
	Developers
	Small Corporates
	Medium-sized Corporates
	Large Corporates
	Mortgages
	Consumer finance
	Credit cards
BBVA Ireland	Financial institutions
	Large Corporates
BBVA Bancomer	Retail Revolving (Credit Cards)
	Large Corporates
	Medium-sized Corporates
BBVA Group	Equity

* Includes Uno-e

The approval of the models by the Bank of Spain includes both own estimations of the probability of default (PD), loss given default (LGD) and the internal estimation of credit conversion factors (CCFs).

In 2014 the Group obtained supervisory authorization to calculate its own requirements for credit risk under the IRB approach for the portfolio of small corporates in BBVA S.A. and the portfolio of large and medium-sized corporates in BBVA Bancomer. The Group maintains its calendar established for receiving approval for additional Advanced Internal Models in different types of risks and geographical areas.

4.5.1.2. Structure of internal rating systems and relationship between internal and external ratings

The Group has rating tools for each one of the exposure categories listed in the Basel Accord.

The retail portfolio has scoring tools for determining the credit quality of transactions on the basis of information on the transaction itself and on the customer. The scoring models are algorithms calculated using statistical methods that score each transaction. This score reflects the transaction’s level of risk and is in direct relation to its probability of default (PD).

These decision models are the basic tool for deciding who should receive a loan and the amount to be granted, thereby contributing to both the arrangement and management of retail-type loans.

For the wholesale portfolio, the Group has rating tools that, unlike scorings, do not assess transactions but rather, customers. The Group has different tools for rating the various customer segments: small corporates, corporates, government and other government agencies, etc. In those wholesale portfolios where the number of defaults is very low (sovereign risks, corporates, financial institutions) the internal information is supplemented by the benchmarks of external rating agencies.

The PD estimates made by the Group are transferred to the Master Scale, enabling a comparison to be made with the scales used by external agencies. This is shown below.

TABLE 33: Master Scale of BBVA's rating

	Probability of default (in basic points)
Internal reduced-list ratings (17 groups)	Average	Minimum from >=	Maximum
AAA	1	–	2
AA+	2	2	3
AA	3	3	4
AA–	4	4	5
A+	5	5	6
A	8	6	9
A–	10	9	11
BBB+	14	11	17
BBB	20	17	24
BBB–	31	24	39
BB+	51	39	67
BB	88	67	116
BB–	150	116	194
B+	255	194	335
B	441	335	581
B–	785	581	1,061
CCC+	1,191	1,061	1,336
CCC	1,500	1,336	1,684
CCC–	1,890	1,684	2,121
CC+	2,381	2,121	2,673
CC	3,000	2,673	3,367
CC–	3,780	3,367	4,243

4.5.1.3. Use of internal estimations for purposes other than the calculation of capital requirements

The Group’s internal estimates are a vital component of management based on value creation, providing criteria for assessing the risk-return trade-off.

These measures have a broad range of uses, from the adoption of strategic business decisions through to the individual admission of transactions.

Specifically, internal estimates are used in everyday business in support of credit risk management through their inclusion in admission and monitoring processes, as well as in the pricing of transactions.

The management use of performance metrics that consider expected loss, economic capital and risk-adjusted return enables the monitoring of portfolios and the assessment of non-performing positions, among others.

4.5.1.4. Process for managing and recognizing the effects of credit risk mitigation

The Group uses risk mitigation techniques for exposures pertaining to the wholesale portfolio by replacing the obligor’s PD with that of the guarantor, in those cases in which the latter is eligible and their PD is lower than the obligor’s.

In retail admission processes, the scoring contains the effect of the guarantor, and the recovery flows that are forthcoming throughout the cycle reflect the recoveries related to the collaterals associated with the contracts. This means that the effect of the collaterals is taken into account in the actual estimation of the loss given default for retail portfolios.

4.5.1.5. Mechanisms used for controlling internal rating systems

The entity carries out the control and monitoring of the rating systems and metrics for risk management for private individuals, SMEs and the self-employed, corporates and institutions. The activities are carried out, within certain analytical and qualitative fields, by realizing periodic 360º monitoring of all impacts of the tools as well as their internal function in terms of efficiency and effectiveness.

Global understanding of the systems allows action plans to be established, with a follow-up to ensure their proper execution. The weaknesses of the rating tools are thus identified and managed. The monitoring function is the main driving force of new developments and evolving maintenance, which allow the business interests of the entity to be aligned with regulatory requirements and management needs within a framework of analytical, technical and technological capacities.

In general, there is a series of corporate management programs that establish the main lines and minimum contents determining the management and/or supervision of the different credit risk models, as well as defining the metrics for their correct control.

More specifically, these corporate management programs will be adjusted to each of the rating tools of a business area within a time horizon adapted to the nature of the tool. Periodically, an overall monitoring and review of compliance with the thresholds agreed under the management program will be carried out to detect situations that could potentially require an adjustment to the models and/or credit policies and to take early corrective actions to minimize the impact of such situations.

Analysis, in the methodological sphere, is defined as the monitoring of the predictive capabilities of the models, backtesting calibration of the parameters, proper granularity and concentration, sample stability of input, as well as traceability, integrity and consistency.

The use of rating systems by the different areas is overseen from the context of integration in management. This context defines parameter sensitivity tests, stress-tests of estimates, proper use of the parameters in the portfolio management to facilitate decision-making, control of exposure without rating, risk policies and the framework for delegating tasks, structures of decision-making committees, implementation risk evaluation, proper technological environment, evaluation of the inclusion of the parameters in corporate applications, proper follow-up of the training of users to collateral its proper implementation and full comprehension, follow-up of the correct structure and quality of documentation, as well as all other activities that ensure the proper use of management metrics.

Apart from the corporate management programs mentioned above, access to the internal rating systems is based on IT system-authorized profiles that ensure only the customer loan management supervisors can see the scoring and rating.

Control of the capital process is performed by risk units that are independent of the units that calculate the scoring and rating and which, therefore, are users of the internal rating system. These control mechanisms are established at different levels of the process, such as at input, execution and final outputs, and involve both the integrity of the data and their accuracy and correctness.

4.5.1.6. Description of the internal rating process

There follows a description of the internal classification processes according to each customer category:

• Central banks and central governments: For this segment, the assignment of ratings is made by the Risk units appointed for this purpose, which periodically analyze this type of customers, rating them according to the parameters included in the corresponding rating model. This model comprises different tools depending on the type of country: developed, emerging or peripheral. Sovereign ratings are generated in local and foreign currency for these three tools, as well as a transfer rating, which evaluates the risk of inconvertibility/transfer restrictions.

In general the rating obtained is based on the ratings of external agencies, where they exist, except for the emerging economies tool in foreign currency. In this case, the ratings are calculated based on an in-house model that establishes a relationship between the score given to each country by the corresponding unit and the empirical PD of the rating agencies. This classifies the countries on the BBVA master scale.

In the case of emerging countries with presence of BBVA subsidiaries or branches, the rating in local currency is adjusted to that obtained by the emerging countries tool under the authorization of the Risk Committee assigned for this purpose.

• Institutions: The rating of Public Institutions is generally provided by the risk units responsible for their approval, on a yearly basis, coinciding with the review of customer risk or with the reporting of their accounts.

In the case of Financial Institutions, the Risk unit responsible makes a regular assessment of this type of customer, continuously monitoring their evolution on domestic and international markets. External ratings are a key factor in assigning ratings for financial institutions.

• Large Corporates: Includes the rating of exposures with corporate business groups. The result is affected both by indicators of business risk (evaluation of the competitive environment, business positioning, regulation, etc.) and financial risk indicators (size of the group by sales, cash generation, levels of debt, financial flexibility, etc.).

In accordance with the characteristics of the large corporates segment, the rating model is global in nature with specific algorithms by sector of activity and geographical adaptations. The rating of these customers is generally calculated within the framework of the annual risk review process, or the admission of new operations. The responsibility for the assessment lies with the units originating the risk, while those approving it validate it when the decision is taken.

• Medium-sized Corporates: This segment also takes into account quantitative factors derived from economic and financial information, and qualitative factors that are related to the age of the company, the sector, management quality, etc. and alert factors derived from risk monitoring. As in the Corporate segment, the rating tends to run parallel to the admission process, so the responsibility for rating lies with the unit proposing the risk, while the decision-making level is in charge of validating it.
• Small Corporates: As in the case of medium-sized companies, this segment also takes into account quantitative factors derived from economic and financial information, and qualitative factors that are related to the age of the company, the sector, management quality, etc. and alert factors derived from risk monitoring. Similarly, the rating tends run parallel with the admission process, so the responsibility for rating is with the unit proposing the risk, while the decision-making level is in charge of validating it.
• Specialist Finance: For classifying this segment, the Group has chosen to apply the supervisory slotting criteria approach, as included in the Basel Accord of June 2004 and in the Solvency Regulations.
• Developers: The rating of real-estate developers allows the rating of both the customers who are developers and the individual real-estate projects. Its use makes it easier to monitor and rate projects during their execution phase, as well as enriching the admission processes.
• BBVA Bancomer companies: This segment also takes into account quantitative factors derived from economic and financial information and bureau information, as well as qualitative factors related to the age of the company, the sector, the quality of its management, etc. The rating tends to run parallel to the admission process, so that responsibility for the rating is with the unit originating the risk, while the decision-making body validates it.

In general in the wholesale area, the rating of customers is not limited to admission, as the ratings are updated according to new information available at any time (economic and financial data, changes in the company, external factors, etc.)

• Retail: This has been broken down into each one of the exposure categories referred to by the correlations provided for in the sections defined in the Solvency Regulations.

One of the most important processes in which scoring is fully integrated at the highest level and in all decision-making areas is the Group’s process for approving retail transactions. Scoring is an important factor for the analysis and resolution of transactions and it is a mandatory requirement to include it in decision-making on risk in those segments for which it has been designed. In the process of marketing and approving retail transactions, the manager is responsible for marketing management, the quality of the risk and the return, in other words, the customer’s comprehensive management, attending to the processes of admission, monitoring and control.

The rating process is as follows for each specific category of retail exposure:

• Mortgages, consumer finance and retail credit cards - Spain: The manager collects data on the customer (personal, financial, banking relationship information) and on the operation (LTV, amount, maturity, destination etc.) and calculates the rating of the transaction with the scoring. The decision of whether it is approved is made based on the results issued by the model.

• Autos Finanzia: The financing application may enter through the call center or be directly recorded in Finanzianet by our authorized dealers. The necessary information on the customer (personal, financial information, authorization of the consult from the external bureau of credit) and on the transaction (maturity, amount, etc.) is recorded to rate the transaction with the scoring. Once the validity of the information provided is obtained, the decision of whether to approve it is made based on the results issued by the model.

• Retail Revolving (BBVA Bancomer credit cards): The manager or specialist party gathers the necessary information on the customer (personal, financial information and authorization of the consult from the external bureau of credit) and on the transaction (limit requested) to rate the transaction with the scoring. There are additional processes for validating and checking this information through the back office or operational support areas. The decision of whether it is approved is made based on the results issued by the model.

• Proactive - Spain: Each month all the customers who have asset positions in credit cards, consumer finance or mortgages and liabilities positions in credit cards and consumer finance, are rated according to information on their behavior.

• Equity: For its portfolio position registered as equity, the Group is applying the rating obtained for customers as a result of their classification in the lending process.

4.5.1.7. Definitions, methods and data for estimating and validating risk parameters

The estimation of the parameters is based on the uniform definition of default established at Group level. Specifically, for a contract or customer to be considered in a situation of default, the provisions of section 4.1.1 must be met, in line with current regulations.

Specifically, there are two approaches within the Group for considering default and estimating parameters:

• The contract-level approach is applied within the sphere of retail risk. Each customer transaction is dealt with as an independent unit in terms of credit risk. Therefore, non-compliance with credit obligations to the bank is handled at the transaction level, regardless of the behavior of the customer with respect to other obligations.
• The customer-level approach is applied to the remainder of the portfolio. The significant unit for defining default is the customer’s sum of contracts, which enter a situation of default en masse when the customer defaults.

In addition, to avoid including defaults for small amounts in the estimations, defaulted volumes are to pass through a materiality filter that depends on the type of customer and transaction.

Estimating parameters

In the case of Spain and Mexico, the Group has an RAR information system that reflects exposure to credit risk in the Group's different portfolios included in advanced internal models.

This information system collaterals the availability of historical data recorded by the Group, which are used to estimate the parameters of Probability of Default (PD), Loss Given Default (LGD) and Credit Conversion Factors (CCF). These are then used to calculate the regulatory capital using the advanced measurement approach, economic capital and expected loss by credit risk. Other sources of information for the Bank may be used in addition, depending on any new needs detected in the estimation process. Internal estimations of the PD, LGD and CCF parameters are made for all the Group’s portfolios.

In the case of low default portfolios (LDP), in which the number of defaults tends to be insufficient for obtaining empirical estimates, use is made of data from external agencies that are merged with the internal information available and expert criteria.

The following shows the estimation methodologies used for the PD, LGD and CCF risk parameters, for the purpose of calculating the capital requirements.

a. Probability of default (PD)

The methodology used for estimating the PD in those cases that have a mass of internal data of sufficient size is based on the creation of pools of exposures. The groups proposed with a view to calibration are defined by pooling contracts together seeking to achieve intra-group uniformity in terms of credit quality and differentiation with all the other risk groups. The largest possible number of pools is defined in order to allow a suitable discrimination of risk. The basic metric used for making these groupings is the score, being supplemented by other metrics relevant to PD that are proven to be sufficiently discriminating depending on the portfolio.

Once the pools of exposures have been defined, the average empirical PD recorded for each one is obtained and adjusted to the cycle. This metric provides stable estimates over the course of the economic cycle, referred to as PD-TTC (Through the Cycle). This calculation considers the portfolio's track record and provides long-term levels of PD.

In low default portfolios (LDPs) the empirical PDs observed by external credit assessment institutions are used to obtain the PD of internal risk groups.

Finally, in customer-focused portfolios there is a Master Scale, which is simply a standard and uniform rule for credit levels that makes it possible to make comparisons of credit quality in the Group’s different portfolios.

b. Loss given default (LGD)

As a general rule, the method used to estimate LGD in portfolios with a sufficient number of defaults called the Workout LGD. Here, the LGD of a contract is obtained as a quotient of the sum of all the financial flows recorded during the recovery process that takes place when a transaction defaults, and the transaction’s exposure at the time of the default.

This estimate is made by considering all the historical data recorded in internal systems. When making the estimates, there are transactions that have already defaulted but for which the recovery process is still ongoing. The loss given default recorded at the time of the estimate is therefore higher than it will ultimately be. The necessary adjustments are made in these cases so as not to distort the estimate.

These estimates are made by defining uniform risk groups in terms of the nature of the operations that determine loss given default. They are made in such a way that there are enough groups for each one to be distinguishable and receive a different estimate.

In keeping with the guidelines set out by the rules, the estimates are made by distinguishing between wholesale and retail type exposures.

There is insufficient historical experience to make a robust estimation in low default portfolios (LDP) using the Workout LGD method, so external sources of information are used, combined with internal data to provide the portfolio with a representative rate of loss given default.

The loss given default rates estimated according to the internal databases the Group holds are conditioned to the moment of the cycle of the data window used, since loss given default varies over the economic cycle. Hence, two concepts can be defined: long-term loss given default, referred to as Long-Run LGD (LRLGD), and loss given default in a period of stress in the cycle, called Downturn LGD (DLGD).

LRLGD is calculated by making an adjustment to capture the difference between the loss given default obtained empirically with the available sample and the average loss given default observed throughout the economic cycle if the observation is complete.

In addition, the LGD observed in a period of stress in the economic cycle (DLGD) is determined.

These estimates are made for those portfolios whose loss given default is noticeably sensitive to the cycle. The different ways in which the recovery cycles can conclude are determined for each portfolio where this LGD in conditions of stress has not yet been observed, and the level these parameters would have in a downturn situation are estimated.

c. Credit conversion factor (CCF)

As with the two preceding parameters, the exposure at the moment of default is another of the necessary inputs for calculating expected loss and regulatory capital. A contract’s exposure usually coincides with its balance. However, this does not hold true in all cases. For example, for those products with explicit limits, such as credit cards or credit lines, the exposure should incorporate the potential increase in the balance that may be recorded up to the time of default.

In observance of regulatory requirements, exposure is calculated as the drawn balance, which is the real risk at any specific moment, plus a percentage (CCF) of the undrawn balance, which is the part that the customer can still use until the available limit is reached. Therefore, the CCF is defined as the percentage of the undrawn balance that is expected to be used before default occurs.

CCF is estimated by using the cohort approach, analyzing how the exposure varies from a pre-established reference date through to the moment of default, obtaining the average performance according to the relevant metrics.

Different approaches are used for wholesale and retail type exposures. The contract approach analyzes the exposure’s evolution until the contract’s moment of breach of contract, whereas the customer approach analyzes the exposure’s evolution through to the moment of breach by the customer.

Once again, in low default portfolios (LDP) there is insufficient historical experience to make a reliable calculation with the Workout LGD method defined. In this case, too, use is made of external sources that are combined with internal data to provide a representative CCF of the portfolio.

4.5.2. Exposure values by category and obligor grade

The following table presents the information on credit risk by method of internal classifications (IRB) by obligor grade for the different categories of exposure. The information shown is balance-sheet volume, off-balance-sheet volume, exposure, EAD, PD-TTC and Downturn LGD and RW (internal estimates approved by the Bank of Spain):

TABLE 34: Advanced measurement approach: Exposure values by category and obligor grade
2014

(Millions of euros)

Categories of Exposure	On balance sheet reasigned exposure (1)	Off balance sheet reasigned exposure (2)	Exposure reassigned (3) (1+2)	EAD (4)	PD-TTC (%)	DLGD (%)	RWA	RW (%)
Central governments or central banks	4,153	749	4,902	4,529	1.3%	35%	376	8.31%
From AAA to AA-	2,335	188	2,523	2,433	0.0%	30%	64	2.63%
From A+ to A-	1,278	464	1,742	1,509	0.1%	41%	65	4.31%
From BBB+ to BBB-	316	59	375	345	0.1%	43%	27	7.71%
From BB+ to BB-	110	30	141	124	1.1%	30%	74	59.73%
From B+ to B-	15	0	16	16	5.5%	27%	8	52.85%
C	51	5	56	54	13.2%	54%	132	245.72%
D	47	2	49	48	100.0%	19%	6	13.42%
Institutions	105,642	6,338	111,981	109,494	0.5%	17%	12,425	11.35%
From AAA to AA-	20,240	511	20,750	20,554	0.0%	14%	401	1.95%
From A+ to A-	43,966	2,046	46,012	45,088	0.1%	17%	2,855	6.33%
From BBB+ to BBB-	33,505	3,075	36,580	35,552	0.2%	18%	6,007	16.89%
From BB+ to BB-	5,660	631	6,291	5,990	0.7%	16%	1,604	26.77%
From B+ to B-	1,634	57	1,691	1,663	3.3%	15%	624	37.52%
C	425	4	429	427	20.7%	38%	892	209.07%
D	213	14	227	220	100.0%	51%	42	19.26%
Corporates	75,120	53,389	128,508	102,682	12.1%	41%	60,998	59.40%
Of which: SMEs	15,623	2,732	18,356	16,890	39%	44%	11,084	65.62%
From AAA to AA-	11	0	11	11	0%	17%	1	8.08%
From A+ to A-	252	241	493	354	0%	49%	80	22.67%
From BBB+ to BBB-	1,323	729	2,052	1,661	0%	50%	648	39.02%
From BB+ to BB-	3,304	951	4,255	3,756	1%	47%	2,747	73.13%
From B+ to B-	4,125	502	4,628	4,366	5%	37%	4,665	106.85%
C	441	26	467	454	17%	39%	754	165.97%
D	6,166	283	6,449	6,289	100%	47%	2,190	34.82%
Of which: Other	48,402	48,809	97,211	73,596	6%	40%	39,394	53.53%
From AAA to AA-	412	2,413	2,826	1,616	0%	44%	178	10.98%
From A+ to A-	6,602	8,190	14,792	10,678	0%	42%	2,707	25.36%
From BBB+ to BBB-	18,456	27,536	45,992	32,876	0%	41%	13,218	40.21%
From BB+ to BB-	12,759	8,598	21,357	17,298	1%	38%	12,687	73.34%
From B+ to B-	6,161	1,344	7,505	6,757	5%	37%	8,239	121.93%
C	498	324	822	682	16%	40%	1,399	204.99%
D	3,513	404	3,917	3,688	100%	37%	966	26.20%
Of which specialized finance:	11,095	1,847	12,942	12,196			10,520	86.26%
Retail	84,850	11,186	96,037	86,750	7.15%	20.13%	22,957	26.46%
Of which: Secured by real estate	69,880	233	70,113	69,892	6%	19%	10,420	14.91%
From AAA to AA-	36,775	149	36,923	36,782	0%	16%	637	1.73%
From A+ to A-	6,753	23	6,776	6,755	0%	19%	260	3.85%
From BBB+ to BBB-	9,665	41	9,706	9,667	0%	23%	954	9.87%
From BB+ to BB-	6,743	17	6,761	6,744	1%	23%	1,823	27.04%
From B+ to B-	4,878	3	4,880	4,878	5%	23%	3,744	76.77%
C	1,493	0	1,494	1,493	19%	26%	2,243	150.24%
D	3,573	0	3,573	3,573	100%	25%	758	21.20%
Of which: Eligible revolving	6,377	11,566	17,943	9,134	7%	76%	7,203	78.86%
From AAA to AA-	403	2,112	2,515	1,071	0%	41%	12	1.13%
From A+ to A-	63	262	326	167	0%	47%	4	2.34%
From BBB+ to BBB-	371	999	1,370	541	0%	72%	62	11.42%
From BB+ to BB-	1,472	4,071	5,543	2,185	1%	81%	717	32.80%
From B+ to B-	3,092	3,633	6,725	4,071	5%	84%	4,273	104.96%
C	803	489	1,292	927	24%	80%	2,050	221.12%
D	172	0	172	172	100%	82%	86	49.96%
Of which: Other corporates	1,578	519	2,097	1,896	11%	59%	965	50.90%
From AAA to AA-	3	0	3	3	0%	40%	0	4.17%
From A+ to A-	1	1	2	1	0%	49%	0	10.88%
From BBB+ to BBB-	135	103	238	196	0%	55%	44	22.33%
From BB+ to BB-	628	246	874	778	1%	57%	374	48.03%
From B+ to B-	612	161	773	715	4%	57%	498	69.67%
C	27	5	32	30	18%	60%	31	101.47%
D	172	3	174	173	100%	77%	18	10.58%
Of which: Other non-corporates	5,863	259	6,123	5,943	8%	51%	2,471	41.58%
From AAA to AA-	1,457	98	1,555	1,462	0%	46%	71	4.87%
From A+ to A-	332	16	349	333	0%	53%	38	11.37%
From BBB+ to BBB-	770	26	796	780	0%	58%	206	26.39%
From BB+ to BB-	1,026	65	1,091	1,062	1%	55%	618	58.20%
From B+ to B-	1,700	50	1,750	1,726	5%	46%	1,247	72.23%
C	230	3	233	230	21%	52%	272	118.44%
D	349	0	349	350	100%	64%	19	5.36%
Equity PD/LGD Method	6,462	0	6,462	6,462	0.3%	87%	10,417	155.47%
From A+ to A-	0	0	0	0	0%	0%	0	0.00%
From BBB+ to BBB-	6,016	0	6,016	6,016	0%	90%	9,285	154.33%
From BB+ to BB-	118	0	118	118	1%	65%	206	174.55%
From B+ to B-	236	0	236	236	3%	65%	556	236.21%
C	93	0	93	93	38%	65%	370	399.64%
TOTAL BY CATEGORY AND OBLIGOR GRADE	275,075	73,054	348,129	310,032	0.06%	28%	105,275	33.96%

(1) Amount included in the balance sheet accounts, without considering off-balance sheet items. (2) Amount not used included in memorandum accounts corresponding mainly to sums undrawn from credit lines and cards, as well as exposures in letters of credit and documentary credits. (3) This refers to exposure following the application of risk mitigation techniques. (4) Value of the exposure in the event of default.

2013

(Millions of euros)

Categories of Exposure	On balance sheet reasigned exposure (1)	Off balance sheet reasigned exposure (2)	Exposure reassigned (3) (1+2)	EAD (4)	PD-TTC (%)	DLGD (%)	RWA	RW (%)
Central governments or central banks	2,707	808	3,515	3,115	1.36	37.55	213	6.84%
From AAA to AA-	1,097	153	1,250	1,177	0.02	31.14	37	3.14%
From A+ to A-	1,147	527	1,674	1,409	0.10	42.57	7	0.52%
From BBB+ to BBB-	337	104	441	389	0.17	40.02	32	8.11%
From BB+ to BB-	53	22	75	64	0.79	21.47	46	72.47%
From B+ to B-	37	0	37	37	6.52	42.99	55	148.16%
C	0	0	0	0	21.16	40.23	0	228.57%
D	36	2	38	37.22	100.00	46.43	36	95.42%
Institutions	80,993	8,161	89,155	85,558	0.57	19.33	12,403	14.50%
From AAA to AA-	11,242	415	11,657	11,459	0.04	17.78	246	2.15%
From A+ to A-	37,307	1,464	38,771	38,189	0.08	17.43	2,346	6.14%
From BBB+ to BBB-	24,221	6,010	30,231	27,534	0.25	23.71	6,437	23.38%
From BB+ to BB-	6,516	215	6,731	6,640	0.79	13.65	1,731	26.06%
From B+ to B-	1,122	40	1,162	1,142	3.54	13.29	501	43.88%
C	381	2	383	382	21.21	44.86	975	255.32%
D	206	14	221	211.96	100.00	42.89	167	78.94%
Corporates	63,196	49,507	112,703	89,644	14.09	40.60	56,098	62.58%
Of which: Total exposures assigned to obligor grades or pools of exposures.	52,388	47,165	99,553	77,454	14.09	40.60	44,931	58.01%
From AAA to AA-	983	2,660	3,643	2,334	0.03	39.45	251	10.77%
From A+ to A-	3,354	11,897	15,251	9,510	0.08	42.33	2,305	24.24%
From BBB+ to BBB-	17,547	21,872	39,419	29,335	0.22	36.98	10,085	34.38%
From BB+ to BB-	10,812	7,512	18,324	15,002	0.73	41.00	10,044	66.95%
From B+ to B-	8,489	1,951	10,441	9,479	5.04	40.51	11,956	126.13%
C	1,616	394	2,010	1,808	14.70	38.31	3,205	177.26%
D	9,587	879	10,465	9,987	100.00	49.74	7,084	70.94%
Of which: Specialist finance	10,808	2,341	13,149	12,190			11,167	91.61%
Retail	84,850	11,186	96,037	86,750	7.15	20.13	22,957	26.46%
Of which: Secured by real estate collateral	72,446	383	72,829	72,470	6.85	14.21	12,727	17.56%
From AAA to AA-	26,246	231	26,477	26,261	0.03	11.00	296	1.13%
From A+ to A-	12,570	46	12,616	12,573	0.07	11.86	298	2.37%
From BBB+ to BBB-	12,286	73	12,359	12,291	0.23	15.30	896	7.29%
From BB+ to BB-	9,877	27	9,903	9,879	0.83	16.23	1,874	18.97%
From B+ to B-	4,968	4	4,972	4,968	5.68	19.85	3,503	70.50%
C	2,393	2	2,395	2,393	18.78	21.02	2,846	118.92%
D	4,106	-	4,106	4,106	100.00	22.97	3,015	73.43%
Of which: Qualifying revolving retail	6,544	10,616	17,160	9,273	6.90	77.26	7,650	82.50%
From AAA to AA-	271	1,773	2,044	794	0.03	41.94	9	1.14%
From A+ to A-	96	344	440	199	0.06	42.70	5	2.34%
From BBB+ to BBB-	367	1,019	1,386	542	0.27	67.85	58	10.72%
From BB+ to BB-	1,711	3,844	5,555	2,498	1.01	79.44	819	32.80%
From B+ to B-	2,998	3,018	6,016	3,959	5.00	84.37	4,205	106.23%
C	928	617	1,545	1,110	21.88	81.82	2,444	220.17%
D	172	1	172	172	100.00	85.11	110	63.97%
Of which: Other retail assets	5,860	187	6,048	5,006	11.98	45.27	2,580	51.53%
From AAA to AA-	895	-	896	895	0.03	45.73	45	5.03%
From A+ to A-	214	-	215	214	0.07	59.24	24	11.42%
From BBB+ to BBB-	628	56	683	656	0.22	57.48	172	26.21%
From BB+ to BB-	1,337	66	1,404	417	0.87	56.17	238	56.99%
From B+ to B-	2,056	48	2,104	2,086	4.85	33.52	1,574	75.44%
C	312	16	328	320	23.54	51.58	383	119.92%
D	417	1	419	417.91	100.00	60.98	144	34.41%
Equity PD/LGD Method	7,516	-	7,516	7,516	0.39	81.34	9,872	131.36%
From A+ to A-	238	-	238	238	0.09	65.00	166	69.66%
From BBB+ to BBB-	6,646	-	6,646	6,646	0.19	83.28	8,254	124.19%
From BB+ to BB-	299	-	299	299	0.83	69.41	572	191.60%
From B+ to B-	332	-	332	332	4.32	65.00	880	264.94%
TOTAL BY CATEGORY AND OBLIGOR GRADE	239,262	69,662	308,925	272,582	7.13	28.51	101,543	35.83%

(1) Amount included in the balance sheet accounts, without considering off-balance sheet items. (2) Amount not used included in memorandum accounts corresponding mainly to sums undrawn from credit lines and cards, as well as exposures in letters of credit and documentary credits. (3) This refers to exposure following the application of risk mitigation techniques. (4) Value of the exposure in the event of default.

The information contained in the above tables is set out below in graphic format:

CHART 7: Advanced measurement approach: EAD by obligor category

CHART 8: Advanced measurement approach: Average weighted PD by EAD

CHART 9: Advanced measurement approach: Average weighted DLGD by EAD

CHART 10: Advanced measurement approach: Average weighted risk by EAD

The following table presents the main variations in the year in terms of RWAs for the Credit Risk advanced measurement approach:

TABLE 35: Variations in the period in terms of RWAs for the Credit Risk advanced measurement approach

	Credit risk (IRB)
RWA Dec 13		92,200
Effects	Acitivity	-1,382
	Changes in RW	-8,094
	Regulatory changes	1,418
	Model roll-out	11,085
	Exchange rate	1,729
	Other	-2,098
RWA Dec 14		94,858

The main changes during this year are basically due to:

• Activity: Net effect of the trend in credit activity in Spain and Mexico.
• Changes in RW: Reduction in the weightings after the process of calibration and updating of the parameters, together with the use of new rating and scoring tools.
• Regulatory changes: Basically due to the new elements related to the application of adjustments due to asset correlation.
• Model roll-out: Caused by the entry into advanced portfolio models of both BBVA S.A. and Bancomer companies.
• Exchange rate: Due basically to the depreciation of the euro against the Mexican peso, which has resulted in an increase in EAD and thus in the RWAs, as explained in section 1.5.4.2 of this document.
• Other: Fall caused by the phased-in application of the support factor for SMEs by the other geographical areas where the Group is present.

4.5.3. Comparative analysis of the estimates made

The following charts compare the expected loss adjusted to the cycle calculated according to the Group’s core internal models approved by the Bank of Spain, with the effective loss incurred between 2001 and 2014. They also present the average effective loss between 2001 and 2014 in accordance with the following:

• Estimated expected loss calculated with the internal models calibrated to 2014, and adjusted to the economic cycle (light green line), i.e. the annual average expected loss in an economic cycle.
• Effective loss (light blue dotted line) calculated as the ratio of gross additions to NPA over the average observed exposure multiplied by the estimated point in time severity.⁽¹⁾

(1) The LGD (PIT) methodology allows for a better approximation of observed losses. For more recent years, given that the recovery processes have not concluded, the best estimate of final LGD is included.

• Effective average loss (2001-2014), which is the average of effective losses for each year (light blue solid line).

The effective loss is the annual loss incurred. It must be less than the expected loss adjusted to the cycle in the best years of an economic cycle, and greater during years of crisis.

The comparison has been made for the portfolios of Mortgages, Consumer Finance, Credit Cards (2004-2014 window) and Automobiles (retail) and SMEs and Developers, all of them in Spain and Portugal. In Mexico, the comparison has been carried out for the Credit Card portfolio (2005-2014 window) and SMEs and Large Companies (2006-2014 window). In the categories of Institutions (Public and Financial Institutions) and Corporate, historical experience shows that there is such a small number of defaulted exposures (Low Default Portfolios) that it is not statistically significant, and hence the comparison is not shown.

The charts show that during the years of biggest economic growth, in general the effective loss was significantly lower than the expected loss adjusted to the cycle calculated using internal models.

The contrary was the case after the start of the crisis. This is in line with the major economic slowdown and the financial difficulties of households and companies, above all in the case of developers and construction companies.

The fact that in some portfolios the average observed loss is greater than the estimated loss is coherent with the fact that the observed time window may be worse than what would be expected in a complete economic cycle. In fact, this window has fewer expansive years (6) than crisis years (8). This is not representative of a complete economic cycle.

Retail Mortgages:

CHART 11: Comparative analysis of expected and incurred loss: Retail mortgages

Starting in 2007, the effective losses are above the expected loss adjusted to the cycle, as they are losses incurred in years of crisis. However, the average of effective losses in this period is notably lower than that adjusted to the cycle. This demonstrates the conservative nature of the estimate.

Consumer finance:

The chart shows that during the years of biggest economic growth the effective loss was lower than the expected loss adjusted to the cycle calculated using internal models. The contrary was the case starting in 2007. This is in line with the major economic slowdown and the financial difficulties of households.

CHART 12: Comparative analysis of expected and incurred loss: Consumer finance

Credit cards.

As in the case of Mortgages and Consumer Finance, the observed loss is lower than the Expected Loss adjusted to the cycle calculated using internal models at best periods of the cycle, and higher during its worst periods.

CHART 13: Comparative analysis of expected and incurred loss: Credit cards

Automobiles:

In this case the expected loss adjusted to the cycle continues to be higher than the average effective losses for the last 14 years, which suggests the conservative nature of the estimate.

CHART 14: Comparative analysis of expected and incurred loss: Automobiles

SMEs and Developers:

Once again it can be seen that during the years of biggest economic growth the effective loss is lower than the expected loss adjusted to the cycle calculated using internal models. The contrary was the case starting in 2007. The great difficulties faced by companies, particularly those engaged in development and construction businesses, are reflected in an observed loss higher than the loss adjusted to the cycle estimated by the internal models.

The expected loss adjusted to the cycle is lower than the average effective losses for the last 13 years, which is consistent with the fact that the observed window is worse than what would be expected over a complete economic cycle (more years of crisis than of economic boom).

CHART 15: Comparative analysis of expected and incurred loss: SMEs and Developers

Mexico Credit Cards:

In the case of Bancomer's credit card portfolio we can see how the average Expected Loss for the cycle calculated using internal models is below the average observed losses. The reason is the use of an observation window which is unrepresentative of a complete economic cycle (the estimate would include comparatively more years of crisis than of economic growth).

CHART 16: Comparative analysis of expected and incurred loss: Mexico Credit Cards

Mexico Corporates:

In the case of the Bancomer Corporates portfolio the average Expected Loss of the cycle calculated using internal models is slightly below the average of observed losses. The last two/three years would show a behavior that is very close to this average for the cycle, which corroborates the improvement observed in these portfolios with respect to the worst years of the crisis.

CHART 17: Comparative analysis of expected and incurred loss: Mexico Corporates

4.5.3.1. Impairment losses

The table below shows the balance of specific, generic and country risk allowances for losses, by exposure categories, as of December 31, 2014 and 2013.

TABLE 36: Balance of specific, generic and country risk allowances for losses, by exposure category

(Millions of euros)

Categories of Exposure	Loan-loss provisions
	2014	2013
Central governments or central banks	4	2
Institutions	78	76
Corporates	6,711	6,717
Retail	1,620	1,566
Of which: Secured by real estate collateral	721	676
Of which: Qualifying revolving retail	516	532
Of which: Other retail assets	384	357
TOTAL	8,413	8,362

4.5.4. Weightings of specialized lending exposures

The solvency regulation stipulates that the consideration of specialized lending companies is to apply to legal entities with the following characteristics:

• The exposure is to an entity created specifically to finance and/or operate physical assets
• The contractual arrangements give the lender a substantial degree of control over the assets and income they generate.
• The primary source of repayment of the obligation is the income generated by the assets being financed, rather than the independent capacity of the borrower.

The following table presents the exposures assigned to each one of the risk weightings of the specialized lending exposures as of December 31, 2014 and 2013:

TABLE 37: Exposures assigned to each one of the risk weightings of the specialized lending exposures

(Millions of euros)

Risk weighting	Scale	Original exposure (1)
		2014	2013
1	50%	0	0
	70%	6,158	5,536
2	70%	0	0
	90%	4,530	5,074
3	115%	1,310	1,071
4	250%	488	908
5	0%	457	560
TOTAL		12,942	13,149

(1) Gross exposure prior to the application of risk mitigation techniques.

4.5.5. Risk weightings of equity exposures

The following table presents the exposures assigned to each one of the risk weightings of equity exposures as of December 31, 2014 and 2013.

TABLE 38: Exposures assigned to each one of the risk weightings of the equity exposures

(Millions of euros)

Risk weighting	Original exposure
	2014	2013
Risk Weighting, Simple Method	3,980	830
190%	479	596
250%	3,266	0
290%	134	140
370%	102	93
PD/LGD Method	6,462	7,613
AA	0	0
AA-	0	0
A	0	238
A-	0	0
BBB+	2,982	3,552
BBB	3,022	1,835
BBB-	12	1,260
BB+	40	231
BB	77	64
BB-	0	3
B+	233	15
B	3	317
B-	0	0
C	93	98
Internal Models Method	254	375
TOTAL	10,696	8,818

The main variations in the period are analyzed in section 7 of the report.