DSAA 2018 Special Session

Opportunities and Risks for Data Science in Organizations: Banking, Finance, and Policy

🎯 Aims and scope

In the last decade there has been an explosion in the velocity, variety and volume of administrative data being collected by government and industry. Social media activity, mobile interactions, server logs, real-time market feeds, customer service records, transaction details, information from existing databases – there’s no end to the flood.

The adoption of data science in finance has been aided by the development of cloud-based data storage and the surge of sophisticated (and sometimes free or open-source) analytics tools. A serendipitous confluence of circumstances is leading to a host of new financial applications.
There are many ways in which data science can be applied in the domain, for instance:

a. By capturing and analyzing new sources of data, building predictive models and running live simulations of market events
b. By using technologies such as Hadoop, NoSQL and Storm to tap into non-traditional data sets (e.g., geolocation, sentiment data) and integrate them with more traditional numbers (e.g., trade data)
c. By finding and storing increasingly diverse data in its raw form for future analysis
d. By finding new ways to compute the aggregates required by audit organizations and efficiently create the reports

The rapid innovation has often outpaced our ability to fully understand, manage, and regulate machine learning applications in the financial domain. To make sense of these giant datasets, companies, financial organizations, and policy makers are increasingly turning to data scientists for answers.
In this context, DSAA is a natural environment where data scientists can meet and discuss how we can offer new tools to help finance and banks to benefit of the huge amount of knowledge hidden in the data they own and continue to gather day by day. Another goal of this special session is to identify and explore the unique challenges of applying data science techniques to problems in the financial policy domain. As a community, we have the potential to be a crucial voice in the policy process.

It is planned to launch a special issue of the ACM Journal of Data and Information Quality on the workshop topics, where selected workshop authors will be invited to submit extended versions of their papers.

📝 Topics of interest

i. Data to Drive Revenue
ii. Data Prioritization, Valuation, & Quality
iii. Data Privacy, Security, and Governance in Finance
iv. Data Integration and Migration in Finance and Banks
v. Prescriptive Analytics
vi. Building a Data-Driven Culture in Finance and Banks
vii. Reporting and Unstructured Data
viii. Data Science and Blockchain Technology
ix. Social Media analysis for Banking and Finance
x. Automated Risk Credit Management
xi. Explainable Approaches to AI (XAI), Fair, Accountable, and Transparent AI (FAT)
xii. Causal Learning
xiii. Data driven approaches to financial policy and regulation

📆 Dates

  • Paper Submission | 25 May 2018 [full submission: extended deadline 1st June]
  • Notification of acceptance | 20 July 2018
  • Camera Ready | 3 August 2018
  • Conference | 1 – 4 October 2018

📩 Paper submission instructions

The paper length allowed is a maximum of ten (10) pages, in 2-column U.S. letter style using IEEE Conference template (see the IEEE Proceedings Author Guidelines:

All submissions will be blind reviewed by the Program Committee on the basis of technical quality, relevance to conference topics of interest, originality, significance, and clarity. Author names and affiliations must not appear in the submissions, and bibliographic references must be adjusted to preserve author anonymity. Submissions failing to comply with paper formatting and authors anonymity will be rejected without reviews.

Submissions are available from Easy Chair Please check to choose the correct special session.

Important Policies

Reproducibility: The advancement of science depends heavily on reproducibility. We strongly recommend that the authors release their code and data to the public.
Authorship: The list of authors at the time of submission is final and cannot be changed.
Dual submissions: DSAA is an archival publication venue as such submissions that have been previously published, accepted, or are currently under-review at peer-review publication venues (i.e., journals, conferences, workshops with published proceedings, etc) are not permitted. DSAA has a strict no dual submission policy.
Conflicts of interest (COI): COIs must be declared at the time of submission. COIs include employment at the same institution at the time of submission or in the past three years, collaborations during the past three years, advisor/advisee relationships, plus family and close friends.
Attendance: At least one author of each accepted paper must attend the conference to present the paper.

🗣️ Invited speaker

Živko Krstić

Živko Krstić is a Data Scientist at Crossing technologies. He graduated at the Faculty of Economics & Business – Split. Živko participated in EU FP7 project “FERARI project”. His area of expertise are text analytics and big data analytics. He participated in development of several big data products such as JupiterOne, Pandora Insight. Živko is author of several papers in field of text analytics. He is founder of initiative Data Science Croatia.
His web site

Big Data Analytics in Banking industry
The amount of data stored by banking industry is increasing and provides the opportunity for banks to use big data analytics and improve its businesses. The banking and financial services industry has been one of the biggest adopters of Big Data technologies such as Hadoop. Banking industry is adopting big data technologies because now they can easily and quickly extract information from their data. Benefits of big data technologies can be seen in different case studies ranging from regulatory compliances management to text analysis (sentiment analysis, topic detection), fraud detection, product cross selling.
In this talk several case studies will be presented: reputation analysis and management, security intelligence and fraud management in banking. Modern big data architectures will be presented in addition to case studies.
We will also address future trends from data science and big data spectrum in banking industry.


👥 Organizers

  • Stefania Marrara, Consortium for Technology Transfer C2T, Italy – stefania.marrara[AT] (contact person)
  • Mirjana Pejić Bach, University of Zagreb, Croatia –  mpejic[AT]
  • Matthew J. H. Rattigan, University of Massachusetts Amherst (USA) – rattigan[AT]
  • Antonia Azzini, Consortium for Technology Transfer C2T – antonia.azzini[AT]
  • Amir Topalović, Consortium for Technology Transfer C2T – amir.topalovic[AT]

🎓 Program Commitee

  • Tiziana Catarci, Sapienza Università di Roma, Italy
  • Paolo Ceravolo, Università degli Studi di Milano, Italy
  • Sara Comai, Politecnico di Milano, Italy
  • Alfredo Cuzzocrea, Università degli Studi di Trieste, Italy
  • Fabio Mercorio, Università degli Studi di Milano Bicocca, Italy
  • Giuseppe Psaila, Università degli Studi di Bergamo, Italy
  • Marco Viviani, Università degli Studi di Milano Bicocca, Italy
  • Katerina Marazopoulou (Facebook)

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