Page 139 - Building Big Data Applications

Chapter 7 Banking industry applications and usage 137

shifting from rule-based software systems to artificial intelligence designed and imple-
mented systems which are more robust and intelligent to the antimoney laundering
patterns. As we evolve the systems and mature their actions, these systems are only set to
become more and more accurate and fast with the continuous innovations and im-
provements in the field of artificial intelligence.

Algorithmic trading

Plenty of banks globally are using high end systems to deploy artificial intelligence
models which learn by taking input from several sources of variation in financial markets
and sentiments about the entity to make investment decisions on the fly. Reports claim
that more than 70% of the trading today is actually carried out by automated artificial
intelligence systems. Most of these investors follow different strategies for making high
frequency trades (HFTs) as soon as they identify a trading opportunity based on the
inputs. A few active banks and financial services companies in AI space are: Two Sigma,
PDT Partners, DE Shaw, Winton Capital Management, Ketchum Trading, LLC, Citadel,
Voleon, Vatic Labs, Cubist, Point72,and Man AHL.

Recommendation engines

Recommendation engines are a key contribution of artificial intelligence in banking sector.
It is based on using the data from the past about users and/or various offerings from a bank
like credit card plans, investment strategies, funds, etc. to make the most appropriate
recommendation to the user based on their preferences and the users’ history.
Recommendation engines have been very successful and a key component in revenue
growth accomplished by major banks in recent times. Implementing the bg data platforms
and faster computations, machines coupled with accurate artificial intelligence algorithms
are set to play a major role in how recommendations are made in banking sector.
Cognitive search represents a new generation of enterprise search that uses sophis-
ticated algorithms to increase the relevance of returned results. It essentially moves the
nature of searches from basing relevance on keyword hits to understanding user intent,
observing behaviors, and applying pattern detection to correctly assert the most relevant
pieces of information. Structuring data and finding relations within it can bring
tremendous additional business value. Even more, value can be created by employing
smart analytic tools in combination with machine learning.
Training these algorithms with the valuable expertise of analysts can be a game
changer that allows a bank to differentiate itself and lead to even more educated in-
vestment decisions. The tools to harvest the full potential of data are here today.
Search results being intuitive and cognitive will provide you with filtering and
dashboarding solutions allowing the generation of summarized information from
various sources, in turn enabling users to dive deep into the data for efficient and