**45% lower economic capital requirements on a typical corporate loan portfolio ? yes it is possible with ALGOSAVE proprietary financial technology. And not just during the Tour de France.**

45% lower economic capital requirements on a typical corporate loan portfolio ? yes it is possible with ALGOSAVE Financial Technology combination of

– borrower & seniority specific Loss Given Default (LGD) term-structure, and

– deep financial-analysis driven Default Probability (PD) and Joint-PD correlation term-structure**.**

**Background :**

The main drivers of Economic Capital estimates are :

– Loss Given Default (LGD),

– default probability (PD),

– and at a credit portfolio level, Joint PD correlation.

Let’s review each of those critical component with ALGOSAVE proprietary Financial Technology and **see why and how ALGOSAVE helps banks save substantial capital.**

**1 – Seniority specific and economic-cycle sensitive Point in Time Loss Given Default (LGD) term structure:**

ALGOSAVE addresses the 2 main drawbacks of traditional LGD “Beta” distribution model :

– uni-modality : where empirical evidence show that recoveries are – at least – bi-modal, with typical peaks at 20% and 80%

– difficulty to cope with “point masses” at 0% and 100%, where empirical evidence also show that LGD are often located in those areas.

**Simple solution : ALGOSAVE financial technology GOT RID of LGD Beta model.**

ALGOSAVE proprietary Financial Technology (FinTech) projects corporate financial statements – 100 data points – using Montecarlo simulations. This allows ALGOSAVE FinTech to reflect each corporate specific asset and leverage structure in each of its thousands of Montecarlo simulations paths at 3 different level of seniority : secured, unsecured and subordinated.

For instance, let’s assume that on a given path of its Montecarlo projections, McDonald (**MCD**) net debt is greater than its Enterprise Value in year 1. This comes – for instance – from higher capital expenditure than average. Assuming that, at the same time, MCD must draw on its Revolver Credit Facility – for instance to fund a greater than optimal sales growth rate – ALGOSAVE Financial Technology “pushes” MCD in default on that specific path.

ALGOSAVE then computes recovery using MCD specific debt and MCD specific asset liquidation value upon default . The latter takes into account 2 possible Machine Learning based outcomes : (1) a gone concern liquidation value, where MCD is taken over (at a multiple of sales or EBITDA) and keeps operating – or (2) a fire sale of MCD assets which price depends on the then Machine Learning Based economic cycle.

Following this methodology, this graph depicts the unsecured 1-year LGD distribution of a typical portfolio of 100 corporates selected from ALGOSAVE issuer database. And not Singapore skyline. The x-axis measures the LGD between 0 and 1.0 while the y-axis measures the number of observations of each bar in the histogram

**First observation ? Both the multi-tail and “point mass” nature of ALGOSAVE proprietary LGD distribution clearly shine.**

The blue LGD histogram depicts the LGD distribution for OECD consensus growth scenario with 59% average LGD – close to Basel Foundation 55% LGD and CDS 60% standard ISDA LGD. Examining the red histogram – which depicts the LGD distribution for ALGOSAVE stressed macro economic scenario – it is interesting to notice the marked shift of the LGD distribution from left (15% “blue” LGD) to right (100% “red” LGD). The average ALGOSAVE stressed-scenario LGD stands at 72%.

**2 – Point in Time Probability of Default term structure :**

ALGOSAVE Probability of Default (PD) term structure is calibrated on capital market PD term structure which comes from CDS, bonds and/or secondary loan trading.

It can also be calibrated on ALGOSAVE clients’ internal Point-in-Time default probability term structure.

This calibration gives ALGOSAVE PD term structure its unique “live” flavor which – by the way – is required by new accounting regulations.

**3 – Joint PROBABILITY OF DEFAULT correlation :
**

ALGOSAVE proprietary joint PD correlation technology is a lot lower than its ill-fated CDS and equiy price correlation proxies. Those are full of “market noise” such as iTraxx CDS sector and ETF trading and hide real default correlation.

But, CLO traders and loan/bond portfolio managers are well aware of this phenomenon whereby actual default correlation – to the exception of those in the highly regulated financial sector – is a lot lower than its capital market proxies.

You may be interested by one of our latest posts which dealt with this issue in greater detail. Read it all here.

**Conclusion – ALGOSAVE proprietary Financial Technology helps banks save 45% in economic capital**

Indeed, putting all things together – LGD, PD and Joint PD correlation – where traditional Expected Loss model points toward a **3.30%** Economic Capital requirement (on the sample corporate portfolio), ALGOSAVE proprietary Financial Technology only requires **1.80%** Economic Capital at 99.9% confidence level.

**A 45% saving on Economic Capital !**

**Now, that’s something worth celebrating. And, not just among our Welsh Tour de France cycling fans 🙂**

Very interisting

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Bonjour Bernard. Merci d’apprecier notre article. Pensez-vous que cela interesserait la Societe Generale ou meme d’autres banques francaises ?

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