In times of political and economic change, financial crime and corruption tend to grow fast. The shock of Brexit, terrorist attacks, the revolution in the Islamic world and other factors create an environment that is demanding for change. AI and Analytics driven solutions have been widely adopted across different industries for various purposes. However, only a handful of banks around the world are working with advanced analytics and artificial intelligence technologies to improve their risk and compliance activities. As the world enters into an era of high uncertainty, the upcoming years will see financial institutions adopt and deploy best-in-class analytics powered tools as part of their efforts to remain fully compliant and to combat financial crime.
Senzing, a new artificial intelligence-based (AI) software company, announced its Senzing software product to address the $14.37 billion financial fraud market. Senzing is an IBM spinout that has reinvented entity resolution, which senses who is who in real time across multiple big data sources. Senzing is disrupting the fraud solutions market by offering the first real-time, plug-and-play, AI entity resolution software product for fraud detection, insider threats and more. Now, any company can deploy Senzing to quickly and effectively detect bad actors in their big data. Senzing uses entity-centric learning and other unique techniques to pierce through falsified identities and networks to find criminals.
In the wake of terrorist attacks like those in Paris and Orlando, Fla., domestic and international law enforcement agencies alike are investigating how attackers were able to slip through intelligence-gathering networks and what can be done to prevent future attacks. Big data analytics, machine learning and artificial intelligence technologies offer federal, state and local law enforcement agencies the opportunity to predict the probability of terror attacks based on many factors, according to David Rubal, DLT Solutions' chief technologist of data and analytics and principal data scientist. A data futurist as well as a fellow at the Institute for Critical Infrastructure Technology (ICIT), Rubal said "personal, behavioral, facial recognition, geo-location, social media and financial data" can help government agencies, law enforcement groups and their technology providers make predictions. "Probability and risk is determined based on the intersections of this data and patterns over time," Rubal explained. "Agencies are also using virtual reality, derived from advanced user experiences and gaming, to simulate'life-like' situations for law enforcement to improve predictability and situational awareness when training officers for responding to a terrorist threat," Rubal said.
You're sitting at home minding your own business when you get a call from your credit card's fraud detection unit asking if you've just made a purchase at a department store in your city. It wasn't you who bought expensive electronics using your credit card – in fact, it's been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent?
Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance. Here is a list of data science use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively. Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providing security to customers and employees.