Transamerica – How One Company Is Capitalizing on Big Data Analytics

Companies who want to have continued growth understand a key component of their success will occur when they are able to personalize their customer’s experience, rather than a one-size-fits-all approach. Not only is the personal approach ultimately more efficient for organizations, it also helps increase customer satisfaction, which in turn leads to more growth.

TransamericaOne company, Transamerica, decided to harness big data concepts in order to provide the best possible personal service to its 27 million customer base. Transamerica pulled in data from over 40 sources, including social media, third-party data, customer voice response systems and all its investment, retirement and insurance data sources. By using big data analytics and machine learning concepts, Transamerica was able to identify new data patterns and insights in order to quickly and efficiently develop, test and deploy its predictive models. Now building models takes hours instead of days, allowing for fewer processing cycles and thereby reduced infrastructure demands, while at the same time increasing the personal experience for its customer base.

So what are some of the tools that Transamerica used to accomplish its goals? Transamerica takes advantage of Hadoop, part of the open-source Apache project. By using a Hadoop-based data lake along with a host of other tools, Transamerica found it was able to store vast amounts of data in their distributed computing environment. Of course, security risks are always a factor when dealing with large stores of potentially personal data, so Transamerica’s entire platform was designed in such a way as to maximize appropriate use of data and adherence to legal and regulatory obligations, while minimizing security risks.

Want to know more about how Transamerica and its customers win big with big data analytics? Click here.

What Is Cognitive Banking?

Cognitive banking is the use of advanced technology to make banks more effective. By using machine learning, AI and data science, financial institutions use data gathered from customers and internal sources to optimize processes.

How Does Cognitive Banking Work?

Banks already collect significant data on interactions and events. Cloud-based machines that understand natural language can learn from all of this data. A machine that has accumulated considerable knowledge can give evidence-based advice to both customers and bank employees. By using these techniques, a bank can provide more value to customers while improving its internal procedures.

Benefit to Customers

When a cognitive system analyzes real customer interaction data, it can gain insights from data by identifying patterns that lead to customer satisfaction. When banks allow it to interact directly with their clients, further learning continues with each new interaction. The system can learn how to provide a better user experience. It gives smart advice to users on how to optimize their finances using the products the bank offers. In this way, cognitive systems can provide true value to clients.

How Banks Benefit

Banks that adopt cognitive banking strategies are well prepared to adapt to a changing future. Hard-coding of procedures becomes unnecessary as they are constantly evolving to fit the needs of the bank. Smart machines are on the front line of customer interaction instead of human operators. This both saves money and has the potential to provide a more optimized experience for clients. Smart machines can also learn banking regulations and security protocols to determine in real-time how well the bank is meeting these requirements. Employees receive notifications if the system needs changes to meet regulations or improve security.

Conclusion

In the rapidly evolving banking industry, banks need strategies to stay competitive. The use of cognitive banking techniques improves customer satisfaction while allowing banks to run more efficiently. For more information on cognitive banking, check out this page from IBM.

IBM’s Watson is Becoming a Crime Fighter

Sherlock Holmes and WatsonIn Sir Arthur Conan Doyle’s Sherlock Holmes stories, Dr. Watson was the great detective’s trusted sidekick in fighting crime. Now, with IBM’s help, Watson has become a crime-fighting detective in his (actually its) own right.

IBM’s newest cognitive computing offering is Financial Crimes Insight with Watson, which is designed to help banks spot financial crimes such as money laundering. The mission of this latest incarnation of Watson, the brainchild of the company’s newly formed Watson Financial Services division, is to “[help] organizations efficiently manage financial investigation efforts through streamlined research and analysis of unstructured and structured data.”

This new suite of Watson products is aimed at helping financial institutions manage their regulatory and fiduciary obligations. It’s estimated that by 2020 the world-wide financial services industry will be faced with more 300 million pages of regulations, with the list growing by thousands of additional pages every day. That is, of course, far too much information for any team of human beings to stay on top of. But Watson, with its advanced artificial intelligence, cognitive computing and machine learning capabilities, was designed for exactly that kind of big data analytics.

The system was trained, using 60,000 US regulatory citations, by experts from Promontory Financial Group, a regulatory compliance consulting firm that IBM bought in 2016. The training also incorporates an ongoing review of transactions and cases that involve possible financial crimes. As Gene Ludwig, founder and CEO of Promontory Financial Group explains, “we’re embedding our deep regulatory experience into Watson so that a broader group of professionals can benefit from this knowledge and help their organizations operate more effectively and efficiently.”

These new Watson products are not narrowly focused just on crime, however. The broader aim is to help clients in the financial services industry address a wide range of risk assessment and regulatory compliance responsibilities. For example, in addition to the Financial Crimes Insight with Watson product, IBM is also offering Watson Regulatory Compliance, which focuses on assisting financial institutions in understanding and addressing constantly changing regulatory requirements.

Attend Watson and IBM i at the 2017 Fall Conference and Expo.

New Identity-Access Management Developments: IT in the Banking Industry

Just like in many other businesses, identity-access management is becoming the make-or-break factor for creating dependable IT security in the banking industry. That’s why new technological advancements in access-management strategies for banks are such a hot topic right now.

Ever since The New York Times reported in 2014 that JPMorgan Chase banks suffered a security breach that leaked the details of at least 76 million personal accounts and 7 million small-business accounts, banks have been scrambling to protect their networks better with more strict authentication measures.

BankTo improve identity-access management security, more banks are looking at evolving ways of integrating multi-factor authentication among their network’s users. For example, HSBC bank announced in March of 2016 that they’ll begin using new alternatives to standard password authentication that include both fingerprint scanning and voice-recognition technology to protect online accounts, according to ProofID Ltd.

Meanwhile, the U.S. DOD has started a “soft certificates” test program to evaluate the security of new wirelessly-derived credentials on mobile devices that access some of their private networks. Mobile devices store such soft credentials and use them to encrypt data and authenticate VPNs, for example.

Banks are more than interested in following suit, as evidenced by Payfone’s developments in mobile-payment authentications to create online transactions that they claim aren’t possible to hack or duplicate. Their number of transactions has tripled in just one year as they expand to network with more banks.

FingerprintExpect more fingerprint-activated payment systems to take off as well as more smartphones than just the iPhone and new Samsung models adopt fingerprint-scanning features in the future.