Finance’s Data Science Revolution
While the finance sector continues to be a first mover of data-driven technology adoption, innovation of ML and AI techniques to solve highly complex challenges facing investment managers, lenders, risk managers and compliance divisions is far from reaching its twilight years.
In such turbulent economic times, central banks, bankers, investment managers and economists have never placed such reliance on predictive, optimisation and automation techniques to make better decisions, tackle unforeseen risks and remain competitive on a global scale.
Since the explosion of data creation in the modern era, the unique challenge for decision makers in finance is less about how do we acquire quality data, rather, we have so much data – where do we start?
Financial institutions have a unique opportunity to apply data science strategies to extract hidden opportunities, identify risks, automate workflow processes, and analyse myriad datapoints across almost every part of its business operations. Financial firms are leading the way to enable predictive analytics at scale.
AutoStat® enables users to harness seemingly disparate datasets and information about its customers, markets, investments and business processes in ways that previously required millions of dollars in capital expenditure and technical expertise to enter the world of predictive analytics.
Thanks to AutoStat®’s code-free modelling, automation and simple data integration, users from the Compliance department to Marketing to Equity research are scaling up analytical capabilities using the most cutting-edge ML and statistical techniques.
AutoStat® in Finance
AutoStat®’s vast range of inbuilt algorithms allow users to undertake exploratory analysis, rapid protoyping, deploy and scale data science pipelines. AutoStat can help your organization to:
1. Analyze and interpret disparate financial datasets, from high-speed market ticker data to customer records
2. Test and deploy machine learning models to unlock market opportunities.
3. Conduct sentiment analysis of customer feedback and communications
4. Conduct topic and sentiment analysis of financial reports
5. Build financial models across commodities, foreign exchange, equites and interest rate securities
6. Predict customer churn
7 Clean and consolidate duplicate customer records >>> See “Data Matching Case Study”