The 5th IEEE International
Conference on Data Science
and Advanced Analytics

1–4 October 2018
Turin — Italy

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


14:00 - 16:00, room “Piemonte”
  • 14:00Opening remarks
  • 14:05Keynote presentation: “Big Data Analytics in Banking industry”
    Živko Krstić, Atomic Intelligence
  • 15:00“Determinants of Growth in Micro and Small Enterprises: Empirical Evidence from Jordan”
    Faisal Awartani and Bryanna Millis
  • 15:20“Network data science models to improve credit scoring accuracy”
    Branka Hadji Misheva, Paolo Giudici and Valentino Pediroda
  • 15:40“Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax”
    Priya Mehta, Jithin Mathews, Suryamukhi K, Sandeep Kumar K and Sobhan Babu Chintapalli

Keynote Speaker

Živko Krstić
Atomic Intelligence

“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.

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:

  • By capturing and analyzing new sources of data, building predictive models and running live simulations of market events.
  • 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).
  • By finding and storing increasingly diverse data in its raw form for future analysis.
  • 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

  • Data to Drive Revenue
  • Data Prioritization, Valuation, & Quality
  • Data Privacy, Security, and Governance in Finance
  • Data Integration and Migration in Finance and Banks
  • Prescriptive Analytics
  • Building a Data-Driven Culture in Finance and Banks
  • Reporting and Unstructured Data
  • Data Science and Blockchain Technology
  • Social Media analysis for Banking and Finance
  • Automated Risk Credit Management
  • Explainable Approaches to AI (XAI), Fair, Accountable, and Transparent AI (FAT)
  • Causal Learning
  • Data driven approaches to financial policy and regulation


Stefania Marrara

Mirjana Pejić Bach

Matthew J. H. Rattigan

Antonia Azzini

Amir Topalovic

Contact Persons

Stefania Marrara –
Mirjana Pejić Bach –
Matthew J. H. Rattigan –
Antonia Azzini –
Amir Topalovic –

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