Banking

Analytics
Carsten Krah 0
Model Risk Management - Dünnes Eis

Banken dürfen das Thema Model Risk Management nicht aussitzen – schon wegen der EZB und ihrer Initiative TRIM - Abwarten, verzögern, aussitzen: Die Banken tun seit Jahren nicht viel dafür, sich als aktiver Gegenpart der Bankenaufsicht zu profilieren. Ist ihnen das vorzuwerfen? Schließlich, so scheint es, treibt die Aufsicht auch

Analytics | Fraud & Security Intelligence | Machine Learning
Min-Gi Cho 0
사기 탐지 전략을 강화하는 8가지 방법

최근 금융감독원은 2017년 국내 보이스피싱 피해액이 2,423억원에 달하며, 전년 대비 26% 증가했다고 발표했습니다. 특히 하반기에만 가상화폐를 이용해 148억원이 탈취된 것으로 밝혀지며 논란이 되고 있는데요. 이렇게 IT 기술이 발달함에 따라 신종 자금세탁 수법이 등장하면서 사기 탐지는 더욱 어려워지고 있습니다. 결국 산업에 관계없이 모든 금융 범죄 조사관은 사기 탐지 기술과 전략을 지속적으로 강화하고,

Analytics | Customer Intelligence
Sandra Hernandez 0
Inteligencia Artificial, cada vez más cerca del entendimiento de las emociones

La inteligencia artificial (IA) es un avance tecnológico que sigue creciendo y que se perfila como una metodología para agilizar todo tipo de procesos, ayudando a la recopilación y el reconocimiento de datos y a la predicción de comportamientos hasta convertirse en una solución a necesidades inmediatas. Por eso se

Fraud & Security Intelligence | Machine Learning
SAS Korea 0
미국 금융소비자보호국, 텍스트 분석과 머신러닝으로 불공정 행위에 대응하다

금융소비자보호국, 불공정 행위로부터 소비자 보호 버락 오바마 미국 전 대통령은 2008년 글로벌 금융 위기 이후 소비자 보호를 강화하고, 금융 업계를 규제함으로써 사태 재발을 막기 위해 다양한 노력을 기울였습니다. 그 중 하나가 바로 2011년 월스트리트에 대한 연방 감독 기구로 출범한 미국 금융소비자보호국(CFPB; Consumer Financial Protection Bureau)인데요. 금융소비자보호국은 소비자를 불공정 행위, 사기, 권력

Advanced Analytics | Analytics | Data Management | Fraud & Security Intelligence
Sandra Hernandez 0
Fintech, de rivales a aliadas de los bancos tradicionales

  Hace un poco más de un lustro, cuando las Fintech –startups tecnológicas que operan en el sector financiero- comenzaron a sonar fuerte en el mercado, se creyó que eran una amenaza para el tradicional sector financiero, su modelo de negocio y las compañías que allí operaban; hoy, lejos de ser calificadas como el enemigo, se han convertido en el

Advanced Analytics | Analytics | Risk Management
Hartmut Kömme 0
Was bringt die Zukunft? Auswertung von Risiko-Szenario-Pfaden

Die Abbildung einer möglichen zukünftigen Situation ist eine Kernaufgabe des Risiko-Managements. Dazu werden unterschiedlich komplexe Modelle mit möglichen Szenarien durchgerechnet. Hierbei erhält man pro Szenario und Vorhersage-Zeitpunkt ein Ergebnis. Bei mehrperiodischen Vorhersagen (z. B. bei Kredit-Portfolien oder Lebensversicherungen) gibt es pro Szenario einen Ergebnis-Pfad. Im Folgenden zeige ich, wie man mit

Analytics
Sandra Hernandez 0
Del Blockchain, el Bitcoin y las Petromonedas a la Analítica de SAS

No cabe duda que la tecnología Blockchain está llamada a ser una de las grandes revoluciones en el mundo en los próximos años. Se ha llegado a afirmar que hará por las transacciones lo que internet ha hecho por la información y las comunicaciones; va a cambiar definitivamente la forma

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Little things go a long way

In my previous post, I described a new options to control the widths of the caps for Whiskers, Error and Limit bars.  This topic could have been titled "Little things go a long way", as such details really make for a good graph. In a similar manner, another detail issue

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Spark table

In the previous post, I discussed creating a 2D grid of spark lines by Year and Claim Type.  This graph was presented in the SESUG conference held last week on SAS campus in the paper ""Methods for creating Sparklines using SAS" by Rick Andrews.  This grid of sparklines was actually the

Analytics | Fraud & Security Intelligence
Veena Hirannaiah 0
Combat wire fraud with analytics

As the banking industry continues to combat increasing fraud challenges, payment fraud is growing exponentially. This growth stems from a shifting payment landscape with new and varied payment options. Globally, governments are introducing new initiatives like faster payments and real-time payments which compress turnaround times. These initiatives are altering the

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Spark grid

The 25th annual SESUG conference was held at in the SAS campus this week.  I had the opportunity to meet and chat with many users and attend many excellent presentations.  I will write about those that stood out (graphically) in my view. One excellent presentation was on "Methods for creating

Fraud & Security Intelligence | Machine Learning
Min-Gi Cho 0
금융 사기 탐지를 위한 머신러닝 핵심 요소

현대 기업에게 금융 사기, 이상 거래 탐지는 분명 어려운 도전과제입니다. 실제 사기 거래 발생률은 낮고 기업 활동의 극히 일부분에 해당되지만, 문제는 적절한 툴과 시스템을 갖추지 않는다면 엄청난 금전적 손실을 야기하는 범죄로 빠르게 이어질 수 있다는 것입니다. 더군다나 금융 사기 범죄자들은 계속해서 새로운 사기 수법을 고안해내고 점차 정교해지고 있는데요. 한가지 좋은

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Legend order redux

Once in a while you run into a pesky situation that is hard to overcome without resorting to major surgery.  Such a situation occurs when you have a stacked bar chart with a discrete legend positioned vertically on the side of the graph.  A simple example is shown below. title

Analytics | Artificial Intelligence | Machine Learning
Christian Engel 0
Künstliche Intelligenz (KI) in der Bankbranche: Herausforderungen und Möglichkeiten (Teil 2)

Bankkunden werden auch immer anspruchsvoller. Im Zeitalter von Google, Apple, Facebook und Amazon haben wir uns daran gewöhnt, personalisierte Angebote auf Basis der von uns freiwillig zur Verfügung gestellten Daten zu erhalten. Hier ist sie wieder, unsere Chatbot-Idee aus meinem 1. Beitrag zu KI in der Bankbranche, und es gibt

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Legend items

Plot statements included in the graph definition can contribute to the legend(s).  This can happen automatically, or can be customized using the KEYLEGEND statement.  For plot statements that are classified by a group variable, all of the unique group values are displayed in the legend, along with their graphical representation

Analytics | Artificial Intelligence | Machine Learning
Christian Engel 0
Künstliche Intelligenz (KI) in der Bankbranche: Ein Fallbeispiel (Teil 1)

Selbstfahrende Sport Utility Vehicle auf unseren öffentlichen Straßen, Siri immer im Zugriff, Alexa im Wohnzimmer … Künstliche Intelligenz und die dahinter funktionierenden Machine-Learning-Verfahren begegnen uns bereits heute, zum Teil eingebettet in den Alltag, zum Teil mit unserem „Wow“, wenn die US-Verkehrsaufsichtsbehörde NHTSA bestätigt, dass es bei einem Unfall mit einem selbstfahrenden

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