Essential Takeaways from the Roundtable:<\/span><\/strong><\/p>\n *Credit risk management is the foundation of banking, yet it has never been more difficult. Banks were already under intense pressure to speed-up credit decisions and reduce costs without compromising risk management. The COVID-19<\/strong> global pandemic introduced unprecedented market turbulence and government intervention and provided many examples of howna\u00efvely extrapolating credit risk experience from the recent past to forecast the future is dangerous in a crisis<\/strong>. Expert human judgment proved critical.<\/span><\/p>\n *Banks<\/strong> have spent several years exploring the use of standardized statistical packages and machine learning tools for developing and maintaining Credit Risk Models, but the process remains manual, time-consuming (up to 6+ months per model for regulated models), labor-intensive, and costly (0.25-1million \u20acto build, plus more for maintenance). Each Bank uses hundreds or even thousands of models, requiring support from hundreds of internal experts across multiple functions.<\/span><\/p>\n *The pandemic highlighted how during a crisis, backward-looking models can quickly become out of date and dangerously inaccurate, or even fail entirely. By the time enough new data were available to rebuild the models, events had usually moved on once more. Unexplainable AI models and automation tools were not the solutions: it was essential to supplement historical data with expert<\/strong> judgment.<\/span><\/p>\n *Faster, more accurate, more forward-looking credit risk modeling remains a priority, and digitization, automation, and AI remain important components of any solution. However, the crisis has reinforced that credit risk management is about more than just historical data and statistical modeling. The next generation of tools for credit risk modeling must also integrate human judgment and credit experience.<\/span><\/p>\n Participants in the Roundtable included some of the biggest banks in the Iberia Region<\/span><\/strong>:<\/span><\/p>\n Abanca, Bankia, BBVA,Millenium BCP Bank, Caixa Geral de Dep\u00f3sitos, ING Spain, Credibom,Cr\u00e9dito Agricola, Natixis, Barclays Portugal, Haitong Bank Portugal, Unicre \u2013 Institui\u00e7\u00e3oFinanceira de Cr\u00e9dito, S.A., Eurobic Bank, kutxabank, Montepio and other ecosystem players like Accenture, Bluecap, APB – Associa\u00e7\u00e3o Portuguesa de Bancos.<\/span><\/p>\n Quotes:<\/span><\/strong><\/p>\n “The Covid crisis<\/strong> highlighted the need for banks to accelerate the development of forward-looking models, including new sources of information. These sources include both internal information, such as transactional data e.g. related to credit card usage, or device-related information; as well as external, industry-related, or based on partnerships with 3rd parties. There are two main challenges to doing this – the time is taken to incorporate and update new types of data and the willingness of the regulators to consider these new types of model.”<\/span><\/em><\/p>\n Mercedes Morris Mu\u00f1oz<\/span><\/strong>, Managing Director of GRM Retail Credit Risk,\u00a0<\/span>BBVA<\/span><\/strong><\/p>\n “The Covid crisis has been extremely disruptive and Millenium BCP has been the leader in supporting the customers in Portugal. It is clear that all banks now have to work on improving and automating credit risk models to ensure those models are quickly adapted to the new context and the information available.”<\/span><\/em><\/p>\n Jos\u00e9 Miguel Pessanha<\/span><\/strong>, Executive Board Member and Chief Risk Officer,\u00a0<\/span>Millennium BCP Bank<\/span><\/strong><\/p>\n “The Covid<\/strong> crisis has forced all banks to adjust for the moratoria granted to customers and to address the inadequacy of backward-looking credit risk models. At Caixa Geral, we are incorporating new data including feedback from customer surveys and accelerating the digital transformation of our approach to credit risk modeling.”<\/span><\/em><\/p>\n Vera Cardoso Santos,\u00a0<\/span><\/strong>Deputy Director for Risk Management,\u00a0<\/span>Caixa Geralde Dep\u00f3sitos<\/span><\/strong><\/p>\n “It is as much a question of what credit risk models<\/strong> can do for the data, as what data can do for credit risk models. Banks should stop dodging the lack of availability of data or budget and resolve data flows by working across business manager, credit risk modelers and IT.”<\/span><\/em><\/p>\n Josep Nadal<\/span><\/strong>, Lead Partner for Financial and Risk Consulting in Iberia in\u00a0<\/span>Accenture\u00a0\u00a0<\/span><\/strong><\/p>\n “Forward-looking models are essential to improving dynamic credit<\/strong> risk modeling, but many banks lack multi-disciplinary teams, accessibility of data and adequate governance around the modeling activity.”<\/span><\/em><\/p>\n Antoni Vidiella<\/span><\/strong>, Partner at\u00a0<\/span>Bluecap Management Consulting by Globant<\/span><\/strong><\/p>\n \u201cThe Covid crisis provided a dramatic illustration of why automated statistical modeling alone cannot solve the industry\u2019s problems with credit risk modeling. Credit risk is multidimensional, and a new generation of tools are required that integrate automation, statistical modeling, data science, AI, credit modeling knowledge, regulations, and, most importantly, human judgment and expertise.\u201d<\/span><\/em><\/p>\n Julian Phillips<\/span><\/strong>, COO and Head of Data Science at\u00a0<\/span>SPIN Analytics<\/span><\/strong><\/p>\n “While Covid has given all of us a stressful last 12 months, one of its “silver linings” has been to highlight the need for banks<\/strong> to accelerate credit risk model development and updating incorporating new data, as many of the roundtable participants confirmed. Ever since we started building credit risk models over 25 years ago, data availability and cleansing have always been the major challenge to model building. Now that, as we heard, explainable AI technology can be teamed with credit experts, hopefully, this challenge will soon be consigned to history.”<\/span><\/em><\/p>\n Andrew Stott<\/span><\/strong>, formerly Head of Western Europe for Oliver Wyman and board member at BBVA, currently Senior Advisor to\u00a0<\/span>SPIN Analytics<\/span><\/strong><\/p>\n <\/p>\n The Department for International Trade (DIT)<\/span><\/strong>\u00a0is a United Kingdom government department responsible for striking and extending trade agreements between the United Kingdom and foreign countries, as well as for encouraging foreign investment and export trade.<\/span><\/p>\n SPIN Analytics <\/span><\/strong>SPIN Analytics helps regulated banks and other credit<\/strong> providers improve credit risk management with its next-generation explainable AI-based platform, RISKROBOT™. To learn more, visit\u00a0<\/span>http:\/\/www.SPIN-Analytics.com<\/span><\/a>.<\/span><\/p>\n On February 24th, 2021the United Kingdom\u2019s Department for International Trade (DIT) and SPIN Analytics convened a Thought Leadership Roundtable to discuss the upheaval in Credit Risk Management with key players from Spain and Portugal\u2019s banking ecosystems (bankers, associations, management consultants, and system integrators). The DIT and SPIN Analytics invited industry experts to share their experience, …<\/p>\n","protected":false},"author":2,"featured_media":8498,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[223,224],"tags":[],"_links":{"self":[{"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/posts\/8494"}],"collection":[{"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/comments?post=8494"}],"version-history":[{"count":5,"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/posts\/8494\/revisions"}],"predecessor-version":[{"id":8503,"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/posts\/8494\/revisions\/8503"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/media\/8498"}],"wp:attachment":[{"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/media?parent=8494"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/categories?post=8494"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ditech.media\/wp-json\/wp\/v2\/tags?post=8494"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/ditech.media\/wp-content\/uploads\/2021\/03\/image003.png\")
<\/p>\n","protected":false},"excerpt":{"rendered":"