Credit Risk and Machine Learning Concepts -7
The anatomy of a Failed company for Credit Risk Associates.
What do we mean ‘fail’?
A customer or entity ‘fails’ when it is no longer able to pay its debts, meet commitments or enters into bankruptcy proceedings that cease operations. The purpose of Credit Risk Analysis is to predict and mitigate such eventuality. Not all models are adequate in predicting failure, with a success rate of between 80% — 90%. For this reason, organizations that face exposure and wish to maximize positive cash flow whilst enabling investment and growth sometimes have to hedge or take measures that guarantee at least some of their Accounts Receivable exposures.
Why the models are not too good to predict some failures:
The main drawbacks of most current credit rating approaches are from two core problems:
1. Timeliness — the Financial performance evaluations are not current, but based on information that is stale, and maybe only available at a yearly basis, some time after the year end. This can be of diminishing value as time goes on.
2. Decontextualization — the importance of the position of the prospective creditor in the supply chain is seldom featured. Whenever considered these factors have traditionally been given a low rating.
Companies having fraudulent activities — Enron, Celadon.
Indianapolis -based Celadon, which ranked as the 18th largest Truck Line Logistics concern with $706 million in Revenue according to Logistics Management’s Annual Ratings, abruptly ceased operations in December 2019. As well as the general poor economic outlooks for freight companies, with 10 operating units of the $519 Million Shevell Group. Celadon was involved in a Financial Reporting scandal that caused two senior executives to be arrested on wire and securities fraud charges. Former management had been replaced after restating earnings from a gross revenue of $1 billion to $706 million. The company had not reported net income figures since 2017. Heavily leveraged and dependent on financing for operations, this was cancelled and the effects of the General Motor’s Strike in the summer of 2019 that reduced the north-south demand for freight capacity. Celadon was founded in 1985 and went public in 1994.
Enron was an energy advisor and market maker in the Natural Gas and Electricity energy markets, that failed in 2001 from poor financial controls and corrupt reporting practices, by creating ‘special purpose entities’ to offload losses from the core company. Earnings were dependent on wholesale trading margins and risk management with a small contribution from physical infrastructure such as pipelines, processing plants, storage facilities and power stations. An article stating ‘Mark-to-Market’ accounting practices were not transparent and should be discounted and the rate of spend of Enron’s invested capital could not be sustainable. Commoditization of Broadband network capacity was a concept that was apparently doomed to fail, and the broadband physical unit was posting figures that did not make sense in comparison to other similar entities and industry competitors. A large number of ‘insider’ shares began being sold from the holdings of the senior executives, even as the stock prices continued to be buoyant.
Companies failing to adjust to markets — Hanjin shipping, Thomas Cook
For example, a well-known and long-established Leisure Travel enterprise, Thomas Cook, had been in operation in the UK for nearly 180 years. It surged in popularity and revenue when it became easy and relatively cheap for UK Citizens to jet to Mediterranean destinations in Europe for short package vacations. Up to the advent of on-line travel agencies, focused package travel away from just Sun and partying, the company was highly successful. However, not able to adapt to the market changes and fill all places to the discount locations, or fully fund the air travel operations from revenue, the company became highly leveraged in debt until it could no longer obtain debt restructuring facilities and ceased trading. An unseasonably hot year in 2018 in the UK reduced the number of holidaymakers needing to seek warmer regions. Additional economic concerns over Brexit lowered the uptake of the company’s packaged holidays into 2019, and that 25% of its revenue or more was needed to service debt interest payments alone.
So this is where AI/ML can bring new light and insight and not rely on overly complex nudges to a score matrix to evaluate, but just come straight out with an answer ‘Credit worthy, Amount, risk level ‘; ‘Credit advisory, Amount, risk level’; ‘Credit Challenged, no Amount, risk level’. Furthermore, to continuously update and monitor for those rated and doing business for the users of this service, in total confidence, by updating as new information is fed into the model in real time and alerts and changes notified to the user organization if significant change is evaluated.
Companies unable to keep cash flow and NOWC high enough are predominantly in the retail sector, such as Sears/Kmart; ToysRUs have had to address and in the case of Toys R Us, not able to withstand the pressures from online retail and general big box stores like Walmart and Target that are able to provide variety at locations that still have foot traffic and guest spends. The regional and local malls are declining and are struggling to retain customer foot traffic and therefore guest spend, and much space is either vacant, used for temporary ‘pop-up’ stores or are getting non-retail use such as leisure, religious groups, and medical facilities where there is still a market and sufficient local wealth for adequate levels of guest spend. The main disruptor to retail is the increasing market share taken by online stores, from Amazon to specialist watch and jewelry sites and entrepreneurs selling their products from Instagram, Facebook and Pinterest as well as other online e-commerce vectors. Other business sectors are also facing splintering in the market, such as package holidays sold by such venerable long-time travel operators including Thomas Cook, which did not address the aging concepts that had supported a boom with more disposable income before the 1990’s and the emergence of eco-tourism, small group vacations and more culture-based rather than party-based vacation needs.
New business ventures have to create a market, show a stable system for operating the business, so may need heavy funding at least for the initial 12 months but sometimes up to the first 10 years to sustain the company. 80% of new start — ups do not last more than the initial 12 months. 30% of businesses that have employees cannot make it past the second year.
This is how Net Operating Working Capital can show the stability of a company, but is only useful to consider at the second year or later in the entities’ existence. The Neural Net Perceptron approach has a node that already lowers the risk rating to a cautious ranking that I am working on in situations where a new entity is seeking credit.
A business fails if they are unable to deliver any real value, either to introduce a new or better product that has a large enough segment of consumers that would use it. A business fails if it has such a product but fails to connect with its target customers. A business will fail if it is unable to turn interest and consumer needs for the product into revenue. That would have to be a continuous loop, so a sales funnel needs to always have entrants that are prospective customers and a process that can classify, initiate interaction, qualify and convert. A business that does not have a clear purpose and management and direction that executes only on that purpose, not the need to claim to be an entrepreneur or business person. Once a market is created, or a market is entered by a new entity, there will be competitors, either existing entities seeking to replace the newcomer, or new entrants who may be able to execute better or faster. The worst factor in a fledgling business is the expense side. It is easy to spend and invest when there is money in the bank, but if the founders or principals want to ‘live large’ and expenses are not essential to the level and size of business, it is impossible for the business to survive. The personal abilities of founders and principals may be perfect to initially launch a product and new venture, but are not necessarily aligned to strategic and effective business leadership and management. Coaching may help, but also the ego must be resilient enough to understand limitations and step away if necessary and allow more competent business managers operate the business day-to-day. Another failure is failure to create a culture with employees and partners, and see them as part of the potential for success rather than units needed to make the system function. Business Systems in addition to Sales Funnels have to be put in place that support the type of business, such as financial accounting, Customer Relationship Management, Human Resource support and benefits management. A business fails if all these are in place but the trust placed in founders and leaders is misplaced by fraudulent, illegal or irresponsible activities not focused on the success of the entity. Yes, go bungee jumping or race a high-altitude balloon around the world, but at least ensure that the entity has potential successors and key-man insurance policies!
This is the 7th installment of my blogs on Credit Risk and Machine Learning. The next installment will consider how an entity may manage risk including the use of Factoring, A/R reporting to a bank for Line of Credit guarantee or Business Loan guarantee and may be found here : https://medium.com/@geoff.leigh19/credit-risk-and-machine-learning-concepts-8-e9884a420a92?sk=bad6d6b53d4dbe36f8da93c8af1a9e8a
The previous 6 installments may be found here:
https://medium.com/analytics-vidhya/credit-risk-and-machine-learning-concepts-3-d2bb2f39d843
