TDWI Konferenz mit BARC@TDWI Track 2019

München, 24. - 26. Juni 2019



Vortrag: Mo 2.4
Datum: Mo, 24.06.2019
Uhrzeit: 17:15 - 18:30

Can AI prevent crimes? A live show

Uhrzeit: 17:15 - 18:30
Vortrag: Mo 2.4


Malicious behavior has become digital: Crimes like credit card fraud & identity theft occur at an unparalleled scale and frequency. Detecting these acts is crucial - but humans are no longer able to keep up with the speed and volume.
Based on our industry experience we built a prescriptive AI to predict & counter crimes in the city of Chicago. The case illustrates the problem of automated resource allocation when dealing with threats.
We share our modelling approach and show an interactive working system based on real live data.
You will get to know our heuristics for tool-selections and how you can transition from aggregated visuals to your own sophisticated machine learning approach.

Target Audience: Everyone interested in AI, or predicting behavior. Especially people from the cyber risk or risk management field
Prerequisites: none
Level: Basic

Extended Abstract
'How can AI systems support humans in risk management?
A critical part of risk management is detecting malicious behavior. We instinctively perform threat assessments by monitoring the behavior of other humans. However, these instincts do not help us in today's digitized world. Can AI help us to discover malicious intent in digital behavior?
This malicious behavior may come in many forms: credit card fraud, identity theft, ransomware etc. The side effects of the digitization do not only include the emergence of new species of malicious acts but also the frequency and scale at which they occur. Detecting these acts has become a crucial activity for any digitized business, such as the financial industry in which the stakes are especially high.
Tackling the task of detecting malicious behavior with humans, who aggregate and visualize data in BI systems to make choices and formulate actions, is not enough:
Humans are biased, limited in their cognitive capacity, very expensive and despite their shortcomings even demand paid vacation.
The industry has reacted to the increased demand for decision making and is now extending their BI products with AI capabilities aiming for decision support. Many products are marketed as all-purpose AI solutions. At the same time, many open source toolkits allow the user to build special purpose AI solutions. In this showcase we will answer the question: Should we strive to build or buy the best all-purpose AI or should we strive to find the best AI-toolkit?
We have been working on multiple projects located in the financial industry aiming to detect malicious activities and want to share our insights with you. We will give you an overview of our findings from a recent project where we identify risky behavior from IT-ticket data.
Since customer data is highly confidential, it is tough to make open-source showcases from real projects. We still want to show a real-world application to our audience, so we built a prescriptive model for malicious human behavior based on public data.
We will address the question if AI can help to prevent actual (physical) crimes in the city of Chicago, a city infamous for its high crime rate. To this end we utilize crime data made available by the Chicago police department and enriched by publicly available weather data.
We show how, by making predictions about future crimes, we can improve the utilization of police resources. This objective is analogous to many business scenarios: It is not enough to make accurate predictions - we need to translate these predictions into problem solving strategies. By doing this we transition from predictive to prescriptive models.
In the live demo we use Splunk as our BI software and Python as our AI backend. We visualize the historic crime data and identify hotspots of crime activity that change over the course of days. It is our goal to identify the hotspots of tomorrow. You will see, how we transition our decision making from aggregated visuals to a more sophisticated machine learning approach. 
In many real-world projects, users are faced with a veritable zoo of possible tools, applications and databases. We will give participants insights into our heuristics for tool selection and our top picks for analytic toolsets. By the end of the talk you will have ideas in mind to answer questions like:
How to best utilize my available data?
How do I select the right toolkit or out-of the box solution?
How to transition from simple to sophisticated AI models?
How to structure AI projects in the real world?


Sponsoren TDWI München 2019

  • Ab Initio Software Germany GmbH Platinsponsor
  • Adastra GmbH Platinsponsor
  • Databricks Platinsponsor
  • Denodo Technologies Platinsponsor
  • Exasol Platinsponsor
  • Fujitsu Platinsponsor
  • INFOMOTION GmbH Platinsponsor
  • itelligence Platinsponsor
  • ITGAIN Consulting Gesellschaft für IT-Beratung mbH Platinsponsor
  • Microsoft Platinsponsor
  • MicroStrategy Platinsponsor
  • NTT DATA Deutschland Platinsponsor
  • SAP Deutschland SE & Co. KG Platinsponsor
  • Sopra Steria Consulting Platinsponsor
  • Talend Germany GmbH Platinsponsor
  • Tech Data GmbH & Co. OHG Platinsponsor
  • Trivadis GmbH Platinsponsor
  • WhereScape Europe Platinsponsor
  • 2150 GmbH | Distilling business insight. Goldsponsor
  • Actian Goldsponsor
  • adesso AG Goldsponsor
  • Alation Goldsponsor
  • ASG Technologies Goldsponsor
  • b.telligent GmbH & Co. KG Goldsponsor
  • blueforte GmbH Goldsponsor
  • CIMACON GmbH Goldsponsor
  • Collibra UK Limited Goldsponsor
  • Cubeware GmbH Goldsponsor
  • Data Virtuality GmbH Goldsponsor
  • Dataiku Goldsponsor
  • Dell Boomi Goldsponsor
  • Deloitte Goldsponsor
  • erwin, Inc. Goldsponsor
  • EVACO GmbH Goldsponsor
  • Genesee Academy Goldsponsor
  • heureka e-Business GmbH Goldsponsor
  • IBM Goldsponsor
  • Informatica GmbH Goldsponsor
  • INFORMATION WORKS GmbH Goldsponsor
  • integration-factory GmbH & Co. KG Goldsponsor
  • Marmeladenbaum GmbH Goldsponsor
  • mayato GmbH Goldsponsor
  • MID GmbH Goldsponsor
  • Neo4j Goldsponsor
  • OPITZ CONSULTING Goldsponsor
  • pmOne Group Goldsponsor
  • Poslovna inteligencija d.o.o. Goldsponsor
  • PPI AG Goldsponsor
  • PROCON IT AG Goldsponsor
  • SISENSE Goldsponsor
  • Snowflake Goldsponsor
  • Syncwork AG Goldsponsor
  • T-Systems Multimedia Solutions Goldsponsor
  • TIBCO Goldsponsor
  • TIMETOACT Software & Consulting GmbH Goldsponsor
  • Vertica Goldsponsor
  • accantec consulting AG Silbersponsor
  • AnalyticsCreator Solutions GmbH Silbersponsor
  • areto consulting GmbH Silbersponsor
  • Attunity Ltd. Silbersponsor
  • Axians IT Solutions GmbH Silbersponsor
  • BARC GmbH Silbersponsor
  • BBF GmbH Silbersponsor
  • biGenius Silbersponsor
  • BIVAL GmbH Silbersponsor
  • brainLight Silbersponsor
  • cimt ag Silbersponsor
  • CINTELLIC Consulting Group Silbersponsor
  • Cognizant Digital Business Silbersponsor
  • Confexx Consulting GmbH Silbersponsor
  • Data Insights GmbH Silbersponsor
  • Data Reply Silbersponsor
  • data42morrow Silbersponsor
  • datasqill - The Post-ETL Silbersponsor
  • Digital Ratio GmbH Silbersponsor
  • Disy Informationssysteme GmbH Silbersponsor
  • Empalis Consulting GmbH Silbersponsor
  • gmc² GmbH Silbersponsor
  • i-refact Silbersponsor
  • initions AG Silbersponsor
  • Lawrence Harvey Silbersponsor
  • linkFISH Consulting GmbH Silbersponsor
  • Lufthansa Industry Solutions Silbersponsor
  • m2data Silbersponsor
  • ORAYLIS GmbH Silbersponsor
  • Passio Consulting Silbersponsor
  • PRODATO Integration Technology GmbH Silbersponsor
  • QuinScape GmbH Silbersponsor
  • Solecon Silbersponsor
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  • TDWI Young Guns Silbersponsor
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  • WE ARE DATA Silbersponsor
  • Woodmark Consulting AG Silbersponsor
  • Contiamo Start-Up
  • DataLion GmbH Start-Up
  • iNNOVO Cloud Start-Up
  • SYNABI Start-Up