Despite the central supporting role of banks during the crisis and the enormous demand for credit, the collapse in earnings has led to a weaker cost-income-ratio (CIR) across Europe. According to our Banking Study 2020 “Banking Efficiency - simplify for the future”, the CIR of European banks rose to 64 percent in the first half of 2020. The pressure on revenue and costs will be further intensified by the ongoing low-interest-rate environment, an increase in credit risk caused by the current pandemic, and a subdued economic outlook. To face these challenges by releasing untapped margin potential, automation, standardization, and innovation based on data and analytics and artificial intelligence (AI) capabilities are essential.
Which measures should you take to overcome all these challenges? Internal and external data can provide you with powerful insights as a starting point to reasonably decide on appropriate actions. There are various data areas for meaningful dashboards, such as external economic and pandemic data combined with internal business data, depending on the individual use case.
Insights on payment behavior are one example. Branch and ATM networks are huge cost items for banks. With the increasing use of online banking channels and alternative payment methods, continuous optimization is critical. The Covid-19 pandemic might accelerate the push towards cashless payments and online banking and, hence, force banks to optimize their channel mix.
Below you can see an example of a crisis dashboard where external Covid-19 data is combined with cash transaction data to generate insights regarding the relation between Covid-19 cases in the different cantons and the customers’ cash transaction behavior. Since the start of the Covid-19, people prefer to pay less with cash and more with card and therefore withdrawing less physical money. If the number of ATM transactions for high-cost ATMs decreases due to the covid-19 situation, these ATMs can be temporarily taken out of use to save costs.
Also, an overview and prediction of the pandemic development in the different locations and regions can help you manage your branches’ safety measures in a forward-looking and efficient way.
Credit risk usually rises for consumer and corporate loans during sharp economic slowdowns, when uncertainty is high, affecting banks and their margins. Hence, a state-of-the-art credit monitoring tool, including machine learning capabilities, can positively impact your credit risk costs. Having a harmonized data set of internal and external credit data at the individual and portfolio level in place is the basis. Then you combine the data set with the right selection of early warning triggers supported by data and analytics and AI capabilities. The derived watchlist helps credit risk experts focus on loans with increasing risk parameters by improving alerts and reducing false positives.
Besides the usual credit metrics, parameters like corporate news flow and social listening should also be included. According to Moody’s Analytics1, credit sentiment scores increase between 6 to 8 months before a major credit event happens and are early warning indicators.
Besides the usual credit metrics, parameters like corporate news flow and social listening should also be included. According to Moody’s Analytics1, credit sentiment scores increase between 6 to 8 months before a significant credit event happens and offer themselves early warning indicators.
To improve loan covenant monitoring efficiency and make it less error-prone, we implemented an innovative text-mining tool for a global German bank. The solution leverages AI capabilities to automate document screening and information extraction.
New digital compliance solutions support proactive prevention and prediction by managing financial resources more efficiently. With enhanced customer due diligence, Banks can use a broad new set of services to manage a client’s entire life cycle, from enrolment to authentication, for day to day usages or critical transactions monitoring. A 360° client view reduces costs by providing a single source of clean, integrated customer data. And anti-money laundry processes supported by machine learning improve the effectiveness and reduce false positives in transaction monitoring.
We combine our data scientists and architects’ knowledge with our broad banking and capital markets experience to find the optimal solution for you during these challenging times. Depending on your company’s maturity level regarding data and analytics and AI, we select the right building blocks and offerings for you to enable insight generation and impact creation. To deliver customized and high-quality solutions to our clients, we collaborate with partners from our strong network of complementary tools and services providers.
1Moody's Analytics, 2021, Credit Sentiment Score™: www.moodysanalytics.com/product-list/credit-sentiment-score