This marcus evans conference will drive initiatives to build an ecosystem of quality data for trade and risk, through embedding data quality efforts within the use of a central data repository, the application of synthetic and legacy data, the role and limitations of machine learning, and use of appropriate data visualisation techniques.
As the volumes of data held by financial institutions increase, so too does the importance of data analytics. The role of data mining within financial institutions has seen a dramatic increase in recent years which shows signs of steadily continuing. Establishing the best practices to facilitate mining and engineering efforts to make your data work for you will be pivotal in ensuring that your institution is the most profitable it can be. With the improvement of data culture and the prevalence of centralised data comes the issues of dirty or noisy data, heterogeneity and the possibility for anomalies, alongside this we are seeing a marked increase in the use of legacy data and external data vendors. These offer some of the biggest challenges for data mining and engineering within financial institutions, but also present the best opportunities for improvement and optimisation. Meeting these challenges and understanding the best way to navigate them could present yet further opportunities to improve your use of data and so increase your overall profitability.
Attending This Premier marcus evans Conference Will Enable You to:
Embed data mining with quality efforts through use of a data lake to set the groundwork for analytics in the financial risk and trading floor
Build synthetic risk and trading data using data engineering to supplement historical and alternative data in financial markets
See how data mining and engineering initiatives have driven machine learning capabilities for quantitative analytics in finance
Apply data visualisation techniques to deduce the usefulness of data in the risk and trading department of financial firm
Learn from Key Practical Case Studies:
Citi looks into the current trends and future developments within data engineering for finance
BAWAG P.S.K. assess methods to adapt the use of historical data whilst avoiding difficulties from legacy systems
ABN Amro consider ways to make the most of your aggregated data and central data repositories
Islandsbanki investigates active data cleaning and anomaly detection to improve data quality
Jupiter Asset Management investigates the possibilities and benefits of advanced data visualisation for mining and analytics
