Tag: credit risk

Why more small businesses should take out Credit Insurance

Author
Angus Dent
Date

Trade Credit Insurance has become an increasingly important part of many businesses’ risk management strategies since it was first developed at the close of the nineteenth century. The sector has grown significantly during this time, as more and more companies have taken up the service, and three main providers have now emerged – Euler Hermes, Atradius and Coface – all of whom focus primarily on Europe, which remains the single largest trade credit insurance market.

But despite the penetration that credit insurance has achieved in Europe as a whole, a 2013 report by the consultancy firm Bain & Company found that only 10-20% of the continent’s SMEs currently utilise this business tool.

credit-insurance-1I believe this to be a real missed opportunity for many small businesses.

The main benefit of Credit Insurance is self-evident: it protects companies from the risk of non-payment by their clients for goods and services that have been provided. The insurer Euler Hermes has calculated that a business’ debtors will often account for up to 40% of its Current Assets and they make the point – borne out by the above statistic – that whilst businesses routinely insure other assets such as their property, this large asset often remains exposed.

Non-payment of a large invoice can cause real damage to SMEs. In fact, nearly a fifth of companies that fail doing so due to bad debt or a lack of working capital. £50k of bad debt to a small business with a 4% profit margin would necessitate an extra £1.25m in sales to compensate for the loss. For any business, let alone an SME, this is clearly a risk worth mitigating.

Taking out Credit Insurance can offer more subtle benefits too. Policies often provide businesses with current and accurate information on their customers, for example, allowing them to judge their financial security. More, taking out credit insurance acts to lower the risk for providers of finance, allowing businesses to borrow at lower rates of interest than would otherwise be possible.

In the current economic climate, improved borrowing opportunities combined with debtor book security is surely too good an offer to pass up.

 

 

 

 

Using unconventional data points to augment risk assessment for consumer lending (P2PL)

Author
Alex Taylor
Date

[avatar user=”Mark Petty” /]

Credit risk modelling is a pervasive and necessary tool used in lending to identity the likely credit worthiness of a Borrower.

Traditional credit agencies rely on historical data to predict the propensity to default over the length of the loan. The data points utilised for consumer credit risk have largely gone unchanged in the last few decades; defaults, credit history and transaction history such as phone, utilities and credit card repayments.

There are clear gaps in this current methodology. Timeliness of data one, but also a gap for the millions of consumers for whom credit agencies do not hold data on.

To have a truly fit-for-purpose risk model for P2P lending there is a need to draw on additional non-traditional datasets for proper assessment. Aggregation of social media, behavioural and big data analysis is key. The traditional credit agencies are talking about it, but like banks they are too slow to move and don’t have the expertise to innovate at the current time.

As we lead our lives online, the opportunities to augment traditional credit reporting with non-traditional sources increases. Everywhere we go we leave a footprint. Our connections on social media and the history of the products we buy can be combined to construct a picture of who we are and our likelihood to payback a loan.

ZestFinance and Kreditech are a part of the new wave of credit agency (and further proof that financial services is being disrupted from all angles). Both utilise big data analytical techniques to mine social media networks and ecommerce transactions.  Kreditech claim to analyse 15,000 individual dynamic data points, contrast that with traditional credit bureaus who have at most 10 to 20 variables.