Page 138 - Building Big Data Applications

136 Building Big Data Applications

Data-driven insights can help you uncover what’s hidden and suspicious, and provide
you insights in time to mitigate risks. For example, analyzing data can help you reduce the
operational costs of fraud investigation, anticipate and prevent fraud, streamline regula-
tory reporting and compliance (for instance, HIPPA compliance), identify and stop rogue
traders, and protect your brand. But this requires aggregating and analyzing data from a
myriad of sources and types and analyzing it all at once, not a small task. Think financial
transaction data, geo-location data from mobile devices, merchant data, and authoriza-
tion and submission data. Throw in data from lots of social media channels and your
mainframe data, and you have a significant challenge on your hands. However, with the
right tools, this melting pot of data can yield insights and answers you never had before,
insights you can use to dramatically improve security, fraud prevention, and compliance.
Theapplicationsherewillleveragebig dataanalyticsandcombine,integrate,andanalyze
all of the data at once, regardless of source, type, size, or format, and generate the insights
and metrics needed to address fraud and compliance-related challenges. For example, you
can perform time series analysis, data profiling and accuracy calculations, data standardi-
zation, root cause analysis, breach detection, and fraud scoring. You can also run identity
verifications, risk profiles, data visualizations, and perform master data management.

Client chatbots for call center

Chatbots are virtual assistants that are designed to help customers search for answers or
solve problems. These automated programs use NLP to interact with clients in natural
language (by text or voice), and use machine learning algorithms to improve over time.
Chatbots are being introduced by a range of financial services firms, often in their mobile
apps or social media. The evolution is in a stage 2 for the chatbots, the potential for
growth has seen an increasing usage, especially among the younger generations, and
become more sophisticated. The stage 1 generation of chatbots in use by financial
services firms is simple, generally providing balance information or alerts to customers,
or answering simple questions. The next generation chatbots in the banking industry will
revolutionize the customer relationship management at personal level. Bank of America
plans to provide customers with a virtual assistant named “Erica” who will use artificial
intelligence to make suggestions over mobile phones for improving their financial affairs.
Allo, released by Google is another generic realization of chatbots.

Antimoney laundering detection

Antimoney laundering (AML) refers to a set of procedures, laws, or regulations designed
to stop the practice of generating income through illegal actions. In most cases, money
launderers hide their actions through a series of steps that make it look like money that
came from illegal or unethical sources are earned legitimately. In a striking move to
detect and prevent AML from occurring, most of the major banks across the globe are