Banking

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

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Tips and tricks: Segmented discrete axis

The previous post on Multiple Blank Categories showed how to include multiple blank categories on the axis.  But, given the purpose for this was to separate different segments in the data, I also included ideas on how to segmented a discrete axis using reference lines or Block Plot.  A similar idea

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Tips and tricks - Multiple blank categories on axis

Off and on, users have expressed the need to include multiple blank categories on a discrete axis.  Often, this is desirable to separate groups of bars (or categories) in a graph due to some difference their definition.  Such a case was discussed in this blog article on using non breaking

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
New Features in SAS 9.40M5 - Gradient fills

ODS Graphics procedures primarily strive towards the following goal:  "Make simple graphs easy and complex graphs possible".   SGPLOT procedure allows you create simple graphs with a single plot statement, and create complex graphs by layering together or combining multiple plot statements.  Generally, the appearance follows the guidelines set by industry

Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
New features with SAS 9.40 M5

SAS 9.4 maintenance release 5 was released on Sept 19, 2017.  This release includes many new items including integration with SAS Viya and SAS Studio, a web application for SAS development.  Also Included with this release are some cool new features in the graphics domain, some of which were requested

Analytics | Data Visualization | Learn SAS | Programming Tips
Sanjay Matange 0
Getting started with SGPLOT - Part 8 - Horizontal HighLow Plot

On a recent visit to an In-House Users Group meeting at a Pharmaceutical company, I presented a 1/2 day seminar on creating Clinical Graphs using SG Procedures.  Polling the audience for their experience with these procedures indicated that many SAS users are not familiar with these new ways to create graphs. So,

Data Visualization
Sanjay Matange 0
Stock chart

In the previous article on Getting Started with Vertical HighLow Plot, I described how we can use the HighLow plot to display the stock price by date.  The HighLow plot is specifically designed for such use cases as shown below. The data is downloaded from the Nasdaq web site, and

Analytics | Risk Management
Hartmut Kömme 0
Risikomodellierung: ein Blick unter die Motorhaube

Modelle im Risikomanagement sind essenziell. Sie helfen uns dabei, das Risiko eines Unterfangens auf Basis weniger Einflussgrößen vorherzusagen. Die Kunst der Modellierung besteht nun darin, die wichtigsten Faktoren zu bestimmen und einen komplexen Zusammenhang vereinfacht so abzubilden, dass die Aussagekraft relevant ist. Das heißt, modellbasierte Prognosen sollen möglichst nahe an

Analytics
Rainer Sternecker 0
Data Preparation – das „Stiefkind“ im Datenmanagement?

Datenaufbereitung, Datenintegration, Datenqualität, Datensicherheit – all das hört sich nach Pflichtprogramm für die IT an und ist längst nicht so sexy wie Hype-Themen à la Data Science, Internet of Things oder Artificial Intelligence. Dass Datenmanagement im Businesskontext aber einen mindestens ebenso großen Stellenwert hat – sei es für die Optimierung

Analytics
Marco Heidelberger 0
Modernisierung im Kreditrisiko - Einschränkungen durch starre Strukturen und Prozesse

Einschränkungen durch starre Strukturen und Prozesse Die Abläufe im Kreditrisiko basieren oft noch auf alten Strukturen und Prozessen. Das betrifft die Methodik und die darunterliegende Technologie, aber auch die organisatorischen Prozesse und Informationsaufbereitung für die Entscheidungsträger. Geschwindigkeit und Flexibilität sind gefragt Dabei werden die Anforderungen der Regulatorik, aber auch der

Analytics | Customer Intelligence
SAS Korea 0
지오로케이션(geolocation), 위치 정보를 이용한 은행 서비스 혁신

은행 산업의 경쟁 환경 변화 지난 4월, 국내 최초 인터넷 전문 은행 ‘케이뱅크’는 출범 사흘 만에 신규 계좌 가입자 수 10만 명을 돌파하며 화려하게 데뷔했습니다. 1992년 옛 평화은행 이후 25년 만에 탄생한 신규 은행으로 은행권은 물론 세간의 이목을 집중시켰는데요. 이어서 7월에는 인터넷 전문 은행 2호 ‘카카오뱅크’가 오픈 8시간 만에 10만 계좌,

Data Management
Rainer Sternecker 0
Bei DS-GVO „Auf-Sicht“-Fahren? Besser nicht!

Die EU-Datenschutz-Grundverordnung kommt näher – ausweichen oder draufhalten? In den letzten Wochen hatte ich die tolle Gelegenheit mit zahlreichen Kunden und Partnern über die neue EU-Datenschutz-Grundverordnung (DS-GVO) zu sprechen. Die Meinungen und Erwartungen sind dabei wirklich außerordentlich breit gefächert. Das ist nicht weiter verwunderlich, denn das Thema hat zuletzt stark an

Advanced Analytics | Analytics | Risk Management
Hartmut Kömme 0
Neue Prozess-Infrastruktur für die Renaissance des Datasteps

Wenn es darum geht, mit Daten zu arbeiten, dann ist der klassische SAS Datastep eines der wirksamsten Werkzeuge, das uns zur Verfügung steht. Es ist sehr einfach, mithilfe von Datenauswahl, Formeln und Bedingungen im Datastep sowie in spezifischen SAS Prozeduren zu Ergebnissen zu kommen. Hieraus sind in der Praxis umfangreiche

Fraud & Security Intelligence
Min-Gi Cho 0
자금세탁방지(AML) 경보 효율성을 높이기 위한 통계적 위험 기반 접근법(Statistical Risk-Based Approach)

지난 4월, 미국 재무부 산하의 금융범죄단속반 ‘FinCEN(Financial Crime Enforcement Network)’이 북한을 자금세탁 및 테러자금조달 위험 국가로 지정하고, 금융 기관에 주의보를 발령했습니다. 트럼프 행정부가 들어선 이후 첫 발령인데요. 미국 우선주의를 제창하는 트럼프 정부와 고조되는 반테러 정서에 따라 미국 금융 당국의 AML 규제는 더욱 심화될 것이란 업계 전망입니다. 실제 최근 스탠다드 차타드

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