Banks and high net clients: the perfect recipe for wealth creation. But it works best when credit risk is viewed through an options lens.
Oct 23, 2025
Matteo Nicoletti
The world of lombard lending: recent trends and current market
In the sophisticated world of banking, many high-value business segments hide behind the well-known classic commercial functions. As a matter of fact, most of this segments represent the real trump cards held by banks, capable of generating immense value every year. Among these segments, Lombard lending is a unique gem. Its name actually originates from the Middle Ages, specifically from Lombard bankers (coming from Lombardy, a region in Nothern Italy), who used to practice a more rustic version of it.
Lombard lending consists of a form of short-term secured lending which banks provide to their HNW (i.e. high-net-worth) clients, granted against movable collateral, namely highly-liquid securities such as stocks, bonds or even insurance policies.
The Lombard lending market has been silently growing in the most recent years, at an annual CAGR (i.e. Compound Annual Growth Rate) of 5% -10% since 2018. These rates enabled it to outpace traditional loans’ market growth, reaching an estimated global value in 2024 of $4,3 trillion, according to a Deloitte research. The financial markets which have been the major drivers of this growth are Switzerland, Luxembourg, the USA, Singapore and the UK.
Of course, this staggering rise has got some well-defined reasons which triggered it: in Europe, the main catalyst for the Lombard lending boom was the prolonged period of historically low interest rates which occurred from 2013 until 2022, in particular the marginal lending facility rate.
Source of data and graphical representation: FRED
The marginal lending facility is the interest rate banks pay when they need to borrow overnight liquidity from their national central bank, using assets as collateral. It is a sort of “Lombard rate for banks”, acting as the ceiling for overnight interbank lending, which means that no bank will lend to other financial institutions at a higher rate. This is clearly consistent with the role of the ECB as lender of last resort. This rate directly influences the wholesale funding costs for banks, consequently affecting the pricing of their own Lombard products for final clients.
Therefore, borrowing liquidity against securities became incredibly cheap. Clients could finalize Lombard loans at low interest rates and reinvest the capital, potentially earning a higher return than the cost of the loan, realizing a more intricate carry trade. Also, instead of selling assets and incurring into capital gains’ taxes, clients could access liquidity in an easier and more efficient way, making their investment strategies even more profitable. This low-rates paradise coincided with the beginning of the digitalization era in finance, making the application and management of these loans quicker and more accessible than ever. Even when rates rose again, starting from the end of 2022, the trend had already been established. As a consequence, in this climate, Lombard lending was a clear win to win segment: clients gained flexible liquidity, while wealth managers could enjoy a highly profitable revenue stream.
Banks' approach to credit risk assessment in lombard lending
Beyond being a significant high value-added segment, lombard lending’s revenue stream represents for banks a generally low-risk source of income. This assumption is based on three credit risk management standard practices adopted by banks:
They tend to lend to very or ultra-HNW individuals.
They focus their attention almost exclusively on the value of the granted collateral, pursuing over-collateralization, namely the provision of collateral whose value exceeds the value of the loan, in order to cover all potential losses in case of default. The difference between the market value of the collateral and the estimation of the latter’s performed by the bank is frequently called haircut. It normally corresponds to the difference between the market value of the collateral and the one of the loan, it serves the purpose to protect from collateral’s market volatility.
They conduct frequent collateral revaluations, focusing on the collateralization ratio, namely the ratio between the collateral value of the loan and the value of the loan itself. Loans that are over-collateralized will have a value greater than 1. In case of necessity to increase the value of the collateral, banks will ask for margin calls, which will reinstate the collateralization ratio to an accepted value. In case the margin call is not met by the borrower, the lender has the right, as per the signed contract, to sell directly the collateral assets on the market. This second step is usually defined as liquidation.
This approach, which evidently concentrates on “preventing” issues through over-collateralization, rather than properly inspecting clients’ creditworthiness, is aimed at maintaining business relationships with these HNW clients. In fact, since these tend to be particularly sensible to sharing confidential financial information, banks are reluctant to conduct deep assessments of creditworthiness in order not to stress them, managing to avoid potential escapes to other institutions. However, from a regulatory point of view, Lombard loans are subject to the same credit risk management requirements as other more conventional types of loans.
In particular:
According to the European Banking Authority’s (EBA) Guidelines on Loan Origination and Monitoring, a focus on creditworthiness is mandatory. This goes definitely beyond a mere evaluation of the collateral.
The IFRS 9 requirement demands, for every significant increase in credit risk, an assessment of the latter at each reporting date, based on any change in the probability of default (PD) over the life of the loan, without considering the expected loss and therefore, implicitly, the collateral.
It appears then fundamental for banks to continuously monitor credit risk exposure in the most adequate and accurate way possible. Today, these complex financial institutions have got plenty of advanced techniques available to assess credit risk, including sophisticated quantitative models based on logistic regression, decision trees and machine learning, which are able to incorporate multiple data coming from historical sets, financial ratios and economic indicators altogether.
A clever options-based approach coming from empirical theory
However, let’s make a step back: in the 70s, Black, Scholes and Mertons’ precious work produced a very innovative and dynamic view of credit risk assessment: analyzing it through the lens of financial derivatives, namely call and put options, which could embody a market-based measure of risk. This approach was certainly ground-breaking at the time; now it has been overcome in terms of efficiency by more advanced models, however it is still able to provide us a clear and dynamic view of how credit risk can be measured. In the case of Lombard lending, the core insight of the options-based approach is that holding risky debt is equivalent to holding riskless debt combined with writing a short put option on the borrower's assets.
In particular:
The riskless debt represents the Lombard loan's principal and interest if the bank had the guarantee of being paid back.
The short put option embodies the credit risk, namely the risk that the borrower defaults, hence he’s not able to fulfill his obligation, or that his creditworthiness worsens while the total refund has not been completed yet. This makes sense since the bank, in a way, "gives" the borrower the right to default, effectively selling a put option. The premium is represented by the credit risk spread charged by the lender, which consists of an addiction to the interest rate.
The event of default occurs when the value of the borrower's assets falls below the value of their liabilities (the loan). At this point, the borrower would effectively "put" his assets to the lender at the strike price (the loan value) and the lender would incur a loss equal to the difference between the loan value and the liquidation value of the collateral. On the other hand, the payoff of the risky Lombard loan can thus be represented as:
Payoff = Riskless Loan Value – Value of Short Put Option on Collateral Assets
This means that quantifying the credit risk of a Lombard loan is equivalent to pricing this incorporated short put option, representing an extremely interesting insight.
However, we need to remember that lombard loans are equipped with a special clause which acts like a saftery trigger: the margin call. In the options world, this gets translated to "down and out" or "barrier" put options. In our case, they are extinguished if the collateral value falls to the barrier level (the loan value) and the borrowers do not meet the available margin calls. At thi point, the rebate (loss) is realized, consisting of the discount applied to the collateral's value during a forced liquidation. Note that this corresponds exactly to the concept of Loss-Given Default (LGD), namely what the bank can recover once the borrower's default is certified. We can observe how this model perfectly mirrors a Lombard lending default: when the collateral value drops to the loan value, a margin call is triggered. If the client fails to meet the call, the bank seizes and liquidates the assets, encountering a loss determined by the unavoidable discount.
What is surely convenient for banks about structuring this model for Lombard loans is that the collateral securities are typically publicly traded and highly liquid. This provides continuous and transparent market data. Hence, the main advantage of this options-based approach is that it generates a forward-looking and market-based Probability of Default (PD) estimation. Unlike static and backward-looking assessments, this model uses real-time market prices, incorporating all available information and future expectations about the collateral's value. This could directly address regulatory requirements like IFRS 9, which mandates assessing changes in credit risk based on PD variations over the life of the loan.
After all, more often than what we think, simple does not mean bad, neither boring or easy. It means clarity.