The Bank organizes its lending portfolio into the following four broad asset categories: Point-of-Sale Loans and Leases, Commercial Real Estate Mortgages, Commercial Real Estate Loans, and Public Sector and Other Financing. These categories have been established in the Bank’s proprietary, internally developed asset management system and have been designed to catalogue individual lending assets as a function primarily of their key risk drivers, the nature of the underlying collateral, and the applicable market segment.

The Point-of-Sale Loans and Leases (“POS Financing”) asset category is comprised of point-of-sale loan and lease receivables acquired from the Bank’s broad network of origination and servicing partners as well as warehouse loans that provide bridge financing to the Bank’s origination and servicing partners for the purpose of accumulating and seasoning practical volumes of individual loans and leases prior to the Bank purchasing the cashflow receivables derived from same.

The Commercial Real Estate Mortgages (“CRE Mortgages”) asset category is comprised of commercial and residential construction mortgages, commercial term mortgages, commercial insured mortgages and land mortgages. While all of these loans would be considered commercial loans or business-to-business loans, the underlying credit risk exposure is diversified across both the commercial and retail market segments, and further, the portfolio benefits from diversity in its underlying security in the form of a broad range of collateral properties.

The Commercial Real Estate Loans (“CRE Loans”) asset category is comprised primarily of condominium corporation financing loans.

The Public Sector and Other Financing (“PSOF”) asset category is comprised primarily of public sector loans and leases, a small balance of corporate loans and leases and single family residential conventional and insured mortgages.

The following table provides a summary of loan amounts, ECL allowance amounts, and expected loss (“EL”) rates by lending asset category:

The Bank’s maximum exposure to credit risk is the carrying value of its financial assets. The Bank holds security against the majority of its loans in the form of either mortgage interests over property, other registered securities over assets, guarantees and holdbacks on loan and lease receivables included in the POS Financing portfolio (note 11).

Impairment – Allowance for Credit Losses

As set out previously, the Bank must maintain an allowance for expected credit losses that is adequate, in management’s opinion, to absorb all credit related losses in the Bank’s lending and treasury portfolios. Under IFRS 9 the Bank’s allowance for expected credit losses is estimated using the expected credit loss methodology and is comprised of expected credit losses recognized on both performing loans, and non-performing, or impaired loans even if no actual loss event has occurred.

Assessment of significant increase in credit risk (“SICR”)

At each reporting date, the Bank assesses whether or not there has been a SICR for loans since initial recognition by comparing, at the reporting date, the risk of default occurring over the remaining expected life against the risk of default at initial recognition.

SICR is a function of the loan’s internal risk rating assignment, internal watchlist status, loan review status and delinquency status which are updated as necessary in response to changes including, but not limited to, changes in macroeconomic and/or market conditions, changes in a borrower’s credit risk profile, and changes in the strength of the underlying security, including guarantor status, if a guarantor exists.

Quantitative models may not always be able to capture all reasonable and supportable information that may indicate a SICR. As a result, qualitative factors may be considered to supplement such a gap. Examples include changes in adjudication criteria for a particular group of borrowers or asset categories or changes in portfolio composition as well as changes in Canadian and US macroeconomic trends attributable to changes in monetary policy, inflation, employment rates, consumer behaviour and geo-political risks.

Expected credit loss model - Estimation of expected credit losses

Expected credit losses are an estimate of a loan’s expected cash shortfalls discounted at the effective interest rate, where a cash shortfall is the difference between the contractual cash flows that are due to the Bank and the cash flows that the Bank actually expects to receive.

Forward-Looking Information

The Bank incorporates the impact of future economic conditions, or more specifically forward-looking information into the estimation of expected credit losses at the credit risk parameter level. This is accomplished via the credit risk parameter models and proxy datasets that the Bank utilizes to develop PD and LGD term structure forecasts for its loans. The Bank has sourced credit risk modeling systems and forecast macroeconomic scenario data from Moody’s Analytics, a third-party service provider for the purpose of computing forward-looking credit risk parameters under multiple macroeconomic scenarios that consider both market-wide and idiosyncratic factors and influences. These systems are used in conjunction with the Bank’s internally developed ECL models. Given that the Bank has experienced very limited historical losses and, therefore, does not have available statistically significant loss data inventory for use in developing internal, forward looking expected credit loss trends, the use of unbiased, third-party forward-looking credit risk parameter modeling systems is particularly important for the Bank in the context of the estimation of expected credit losses.

The Bank utilizes macroeconomic indicator data derived from multiple macroeconomic scenarios in order to mitigate volatility in the estimation of expected credit losses, as well as to satisfy the IFRS 9 requirement that future economic conditions are to be based on an unbiased, probability-weighted assessment of possible future outcomes. More specifically, the macroeconomic indicators set out in the macroeconomic scenarios are used as inputs for the credit risk parameter models utilized by the Bank to sensitize the individual PD and LGD term structure forecasts to the respective macroeconomic trajectory set out in each of the scenarios (see Expected Credit Loss Sensitivity below). Currently the Bank utilizes upside, downside and baseline forecast macroeconomic scenarios, and assigns discrete weights to each for use in the estimation of its reported ECL. The Bank has also applied expert credit judgement, where appropriate, to reflect, amongst other items, uncertainty in the Canadian and US macroeconomic environments.

The macroeconomic indicator data utilized by the Bank for the purpose of sensitizing PD and LGD term structure data to forward economic conditions include, but are not limited to: real GDP, the national unemployment rate, long term interest rates, the consumer price index, the S&P/TSX Index and the price of oil. These specific macroeconomic indicators were selected in an attempt to ensure that the spectrum of fundamental macroeconomic influences on the key drivers of the credit risk profile of the Bank’s balance sheet, including: corporate, consumer and real estate market dynamics; corporate, consumer and SME borrower performance; geography; as well as collateral value volatility, are appropriately captured and incorporated into the Bank’s forward macroeconomic sensitivity analysis.

Key assumptions driving the base case macroeconomic forecast trends include: the Bank of Canada (“BoC”) continuing to tighten monetary policy with the overnight rate reaching 4.5% in early 2023; consumer spending declines and business investment slows as a function primarily of higher rates and persistent inflation; inflation beginning to decelerate consistently in early 2023, in line with the anticipated end of the current monetary policy tightening cycle; the housing market continues to cool attributable to higher interest rates which dampen demand, causing home values to continue to decline; a mild technical recession emerges along with rising unemployment in early 2023 as consumption slows and firms dial back their growth plans; public health restrictions do not return even as new COVID-19 case counts occasionally spike through the winter; and, supply-chain stress continues to ease as global vaccination rates improve and good demand softens.

Management developed ECL estimates using credit risk parameter term structure forecasts sensitized to individual baseline, upside and downside forecast macroeconomic scenarios, each weighted at 100%, and subsequently computed the variance of each to the Bank’s reported ECL as at October 31, 2022 in order to assess the alignment of the Bank’s reported ECL with the Bank’s credit risk profile, and further, to assess the scope, depth and ultimate effectiveness of the credit risk mitigation strategies that the Bank has applied to its lending portfolios (see Expected Credit Loss Sensitivity below).

Expected Credit Loss Sensitivity:

The following table presents the sensitivity of the Bank’s estimated ECL to a range of individual macroeconomic scenarios, that in isolation may not reflect the Bank’s actual expected ECL exposure, as well as the variance of each to the Bank’s reported ECL as at October 31, 2022:

The ECL model requires the recognition of credit losses based on 12 months of expected losses for performing loans which is reflected in the Bank’s Stage 1 grouping. The Bank recognizes a lifetime expected losses on loans that have experienced a significant increase in credit risk since origination which is reflected in the Bank’s Stage 2 grouping. Impaired loans require recognition of lifetime losses and is reflected in Stage 3 grouping.

The determination of a significant increase in credit risk is a function primarily of loan product type and the associated risk profile of same. The principal factors considered in making this determination include relative changes in the Bank’s internal risk rating assignment, the loan’s watchlist status, and the loan’s delinquency status. Notwithstanding the above, the assessment of a significant increase in credit risk will require experienced credit judgement.

b)

Allowance for credit losses:

The following table provides a reconciliation of the Bank’s ECL allowance by lending asset category for the year ended October 31, 2022:

The following table provides a reconciliation of the Bank’s ECL allowance by lending asset category for the year ended October 31, 2021:

c)

Maturities and yields:

Average effective yields are based on book values and contractual interest rates, adjusted for the amortization of any deferred income and expenses.

At October 31, 2022, the Bank held one impaired loan totalling $279,000 ( October 31, 2021 - $nil). The impaired loan was subsequently fully repaid on November 1, 2022.