Fighting Cybercrime With Big Data
Traditionally, cybercrime has been fought through infrastructure. Better firewalls. Better passwords. Better identity management. Cybercrime has historically been the province of technology departments. But is it enough?
The BFSI Cybercrime Environment
Not in today’s day and age. Every day we hear new stories about online financial fraud or other data breaches. According to the 2015 KPMG Cybercrime Survey Report, 72% of all companies have faced a cyberattack in the past year, with 63% of them suffering a financial loss as a result. For 70% of companies in banking and financial services, the direct costs of fraud are as high as 7 basis points. For an institution with 100 billion dollars in assets, that represents a 70 million dollar a year loss.
Not only is cybercrime increasing in absolute numbers, cybercriminals are becoming more effective – and more professional. Previously the domain of bored programming aficionados looking to prove their skills, cybercrime is becoming increasingly professional, with skilled employees doing jobs for large, enterprise type institutions.
The nature of cybercrime is evolving as well. Of attacks instigated against financial services companies, phishing – of both consumer and business emails – remains a popular method of installing malware, spyware or otherwise obtaining access to financial information and assets. The risk is exacerbated by the increasingly popular use of mobile devices in banking transactions.
Given the increasing sophistication of cybercrime, relying strictly on physical infrastructure to prevent breaches is increasingly ineffective. For every patch, upgrade and system implemented, a team of cybercriminals is working to break that security. Moreover, with the prevalence of phishing and social engineering, trusted and authorized users actually enable the breach, enabling the bypass of traditional security measures, even when up to date.
How Big Data Can Help
Today’s new cyber threats and tactics require new strategies. Perimeter protection is no longer enough. Enter big data.
Big data allows banks and financial institutions to analyze enormous volumes of historical transactional data. By analyzing information from within their own environment and across third parties, companies are able to develop predictive models that recognize unusual use patterns which might be indicative of cybercrime. Cross channel pattern recognition coupled with robust detection systems can minimize financial losses by identifying and ultimately preventing transactions when they first occur, rather than waiting for traditional reporting systems, including consumer complaints and loss reports, to identify problems.
Use of big data to prevent fraud varies across institutions, and can range from single point-in-time analysis conducted for specific incidents of fraud, to a program of continuous, repetitive and automated analyses in traditionally high fraud business practices. Particularly in the case of ongoing big data fraud detection programs, results become more accurate, and the program more effective with the aggregation and analysis of greater information stores.
The use of big data in fraud prevention takes cybersecurity from reactive to proactive. This revolutionary approach reduces financial, reputation and business losses frequently associated with cybercrime. While implementing security protocols using big data does involve infrastructure, human capital and project management costs, the savings from minimized actual and ancillary cybercrime costs makes it a smart investment for many entities in the banking, financial services and insurance industry.
Are you ready to leverage big data to protect your financial services environment? Learn more about how SRI Infotech can help.