Whitepapers

HOW CAN ESG DATA HELP REDUCE CLIMATE RISK? Climate change is one of the greatest, long-term risks facing the insurance sector. Fortunately, organizations can play a pivotal role in the transition to a greener economy.

WHY BANKS AND LENDERS MUST LOOK BEYOND THE COST OF LIVING CRISIS TO MEET THE CHANGING NEEDS OF EUROPEANS This year’s report shows that, despite some encouraging signs of improvement, four in five Europeans still have concerns about their finances over the next 12 months. Our report reveals that the cost of living has indeed altered the ways in which people engage with and manage their money, particularly in terms of accessing online banking and other digital services. Many more Europeans are now planning to use digital tools to monitor their finances more closely going forward.

Open Finance and Open Banking are revolutionizing the financial services industry by making structured and especially unstructured data available from multiple sources. Categorization engines enrich financial transaction information by adding a “category”: a name that gives a meaningful description of the nature of the transaction. To accomplish this task, the engine classifies the data according to some sort of criteria, such as merchant, location, or transaction amount.

Through this whitepaper, we have explored how our carbon footprint calculation works and its use cases, as well as the market context for carbon footprint and ESG topics. We have also discussed the key KPIs and aggregated analysis that can be derived from carbon footprint data, and shared insights about the challenges and opportunities associated with carbon footprint management.

Europe is currently in the grips of a cost of living crisis, with millions of beleaguered households on the continent in desperate need of help as spiralling inflation and rising prices play havoc on their finances. The findings expose the level of impact Europeans expect the cost of living crisis to have on their finances. In response, people across these markets told us they plan to take drastic action to rein in their spending, as well as turning to a range of third parties for financial support. Among those they would most likely turn to are their banks and other financial providers.

SMEs, and in particular micro and small companies, are an important part of the European economy. The micro and small business segment includes over 25 million companies, with a total turnover of over €10bn, and employs just under 43% of the total number of employees in the European private sector. Despite the overall size of the segment, these companies have been, up to now, challenging for banks to serve. Due to the small size of these organizations, they are often classified and treated as retail customers. This, coupled with their limited accounting and financial management capabilities, limits their ability to access finance.

Financial services is a data-intensive industry in which banks and insurance companies have historically held large quantities of information. The development of the internet, the increase in new channels, and the introduction of technologies including cookies and trackers have helped not only to create a better user experience, but also to exponentially increase the amount of collected data, for the most part dark data. With the availability of new digital technologies and a higher degree of interconnectivity and interactions, new data sources have emerged that are a potential treasure trove for businesses.

Currently, we live in an era where personalization has become the norm and is expected from banks, as financial services are increasingly driven by highly customized and personalized offerings from not only traditional banks, but also neo banks, FinTech’s, digital financial providers, and even companies outside of financial services such as telcos and retail companies. These opportunities are enabled by technologies, analytic capabilities and endless user information that allow providers to create such personalized experiences that customers love.

The beginning of Open Banking had a variety of industry and commercial impacts, but one of the unintended consequences in consumers’ lives was that they came to realize that their data was theirs and that they could choose who to share it with. That was catalyzed by all the campaign of awareness about the social networks scandals raised on the past, where people started to wake about what other companies where doing with their information and how their were leveraging their business models on all the metadata and insights information behind liking a photo on Instagram, reacting to a publication on Facebook or checking into a place in Foursquare. As a result, in the future, people are going to be more cautious about their personal information and their data, but they will still share it with you if you prove to them that your counterpart is worth the cost.

MACHINE LEARNING FOR CREDIT RISK MANAGEMENT AND IRB MODELS: LESSONS FROM SUCCESSFUL CASE HISTORIES A joint paper by Intesa Sanpaolo and CRIF The use of machine learning (“ML”) models in banks has raised considerable interest and sparked a lively debate, among both scholars and practitioners. In November 2021, the European Banking Authority (“EBA”) has even published a discussion paper (“the Discussion Paper”) on the use of ML for internal ratings-based (“IRB”) models.