Trends like social media have further complicated the assessment and analysis of financial risk. This industry nevertheless remains a core factor in the global marketplace for commerce, investment, and development across all major and developing markets alike. Ensuring effective risk assessments in the finance and banking industries depends on better tools through innovations in Big Data to identify new risk trends and improve risk calculations.
Incorporating social and global data in financial analysis
The growing global prevalence of social media has had a significant impact on risk analysis in the financial sector, a realm where Big Data has enabled more effective engagement, review, and calculations regarding financial risk and social media’s influence. Firms like Infosys, for example, have identified several use cases with Big Data and social media for assessing opportunities, weakness, risks, and other factors in capital markets. Infosys notes the core capability of “Sense & Response Systems” for responsiveness in capital markets and financial environments powered by data analytics at a global social-media powered scale. By incorporating feedback from Facebook News Feeds, personal blogs, Twitter posts, and other sources of user generated content, patterns emerge that allow businesses to assess the marketplace in both the short and long term. This clairvoyance enables better risk assessments by factoring in more statistics on genuine public sentiments when analyzing financial opportunities
Mitigating risk and preventing fraud with Big Data
Numerous banks and financial services firms have taken advantage of Big Data to more directly affect and mitigate risk scenarios with large-scale analytics. In the years since the 2008 global financial crisis, numerous banks large and small have taken the advent of large-scale analytics and concerns over future financial challenges to research tools that can better analyze concerning patterns, identify problems in the market, and ultimately provide better visualizations on market trends, potential financial risks, and similar opportunities and challenges.
Firms like MapR specialize in creating metrics and risk analysis solutions using Big Data and software like Apache Hadoop. MapR in particular works to develop models based on a financial institution’s history, preferred industries, and connects with larger market patterns to determine and calculate risk assessments for the bank.
Many firms have already carried out risk-analysis solutions using Big Data as a core function of the operation. However, given the nature of the business, most clients remain confidential for security and privacy purposes. While software-based providers like MapR are one example of providing financial analysis using analytics, even large, global firms like PricewaterhouseCoopers (PWC) also incorporate Big Data into traditional financial consulting and analysis services. For PWC, the goal of Big Data is to blend its capabilities with existing heuristics and consulting services that the company has been long-known for as one of the largest professional services firms in the world. PWC in particular notes the effectiveness of data driven analytics in several case studies to reduce Anti-Money Laundering riskiness and improve general compliance operations for several clients.
Experimentation with Big Data continues
Big Data analytics is clearly fueling new methodologies and ideas for improving financial analysis to reduce risk, mitigate fraud, and offer more accurate assessments in complex global financial markets. While some firms like MapR focus on data solutions with limited traditional consulting methods, larger professional service firms see Big Data as a supporting tool and role in the tried-and-true methods of market analysis and risk assessment that have been in use for decades.
Ultimately, both firms bring significant improvements to the organizations they support. As the Big Data marketplace continues to become a household name in consulting and problem-solving, even more methods and styles will emerge that incorporate data-driven analytics into calculating risk.