![]() Sector-specific differences were also identified. One study of corporate loans in Portugal concluded that, in 20, especially for longer-term loans, the risk-adjusted return was almost 3 percent too low. On the other hand, this can allow systemic errors to creep in, with a negative impact on the lender’s margin. This is not necessarily a disadvantage: The human factor can be a corrective when it comes to data from manipulated books or unrealistic forecasts regarding sales and growth. In such cases, the decision depends, to no small extent, on the loan officer’s experience, which has a relevant influence on the weighting of the various risk items. It provides the basis for pricing and other credit terms.Ĭommercial lending today is still largely based on a manual process involving in-depth analysis of baseline data and evaluation of soft factors. Risk quantification comprises determining the probability of default (PD), loss given default (LGD) and risk-adjusted return on capital (RAROC). Such automated processes can be used to acquire much more data about a company than before – and thus paint a more complete picture that minimises uncertainties in risk assessment. For instance, frequent complaints about quality defects could be a warning signal regarding a company’s future financial development. Natural language processing (NLP), a branch of artificial intelligence (AI) like ML, can evaluate a customer’s reactions in forums, for example, and thus identify positive or negative changes to their image. However, a complete overview of the financial situation can only be obtained by adding data from various internal and external systems, such as credit agencies, along with qualitative information – from social media, for instance. This means that all customers’ data is available in one uniform format and can easily be processed further. With automated spreading, financial data is captured from financial statements and assigned to the appropriate categories. The use of artificial intelligence (AI) can automate the incorporation and reading of balance sheets. Slow manual processes delay credit decisions and increase costs. Annual financial statements and quarterly reports do provide extensive data on a company’s financial situation, but the acquisition and analysis of this data is often quite an obstacle. The basis for assessing a company’s creditworthiness is balance sheet analysis. In this way, a solid data pool is created, which meets the requirements for all aspects of credit management. The KYC profile can be updated according to risk. Solutions like ACTICO KYC can be integrated into the onboarding process via suitable interfaces and, for example, take over automated comparison of customer data with sanction lists and PEP (politically exposed person) lists, updating of risk classification, or documentation of a company’s beneficial owners. ![]() Beyond that, it offers the opportunity to create a comprehensive customer profile that, if properly maintained, provides all relevant information needed for regular sanction list and PEP screening, or for periodic updating of the credit rating, for instance.Įspecially in the KYC process, together with onboarding, the potential efficiency can be raised considerably by means of digitisation and automation. The KYC process is primarily a regulatory obligation imposed on banks and financial service providers to prevent money laundering and terrorist financing. Customer onboarding and Know Your Customer (KYC) Last but not least, the question of how quickly credit approval is granted can also determine business success.ġ. On the other hand, it is important to make the credit decision and the ongoing monitoring of the borrower more efficient, and to minimise the process costs. On one hand, creditworthiness and risk factors must be determined reliably and priced appropriately – not only in order to demand risk premiums in good time, but conversely to also find opportunities with more favourable conditions that promise competitive advantages. This makes it all the more important to improve credit risk management now, so as to increase earnings from the lending business. In many cases though, it is difficult to assess how a borrower’s situation will develop in the aftermath of the Covid-19 crisis, as there is no empirical data on this so far. ![]() The economy is now picking up again, and with it the need for finance. Where businesses had to close during lockdown, the need for bridging loans increased, while investments in buildings and equipment were postponed. ![]() In addition, the corona pandemic has left its mark.
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