The future successful organisations in the FS industry
will be those who harness the power or text analytics and NLP. Those who ignore
it, do so at their peril. The democratization of Artificial Intelligence,
however, has demonstrated that the challenge for FIs is not the technological
capability but rather the identification of value generating use cases.
NLP libraries like Spacy, Hugging Face and models like
the Googles Switch Transformer or GPT-3 from Open AI allow technologically
skilled teams in Banks to leverage this innovation. The application of transfer
learning significantly lowers the barriers to entry such as data availability
and computing power for training, thus making sophisticated use cases for
smaller FIs possible. On the other hand, the proliferation of no-code or
low-code platforms for Machine Learning & AI bring the technology to the
business users. Medium and smaller FIs can thus generate a competitive
advantage instead of following the market. With this article I demonstrate
several use cases from the industry to serve as ideas for these FIs to help in
this mindset shift.
Several Techniques can
be used in navigating the capital markets by leveraging unstructured data.
a) Key Phrases
phrases from large text to generate a summary.
b) Sentiment Analysis/ Text Classification
sentiment of text based on topics, entities, or temporal direction of
statements to generate more granular insights.
c) Temporal Analysis
temporal direction of the statements to evaluate the relevance.
d) Topic Extraction/ Topic Modelling
statements regarding a topic of interest for your analysis or model the
underlying topics in large text.
e) Q&A – Semantic Search
search based on semantic similarity rather than exact keyword matches.
Use Cases in Due Diligence
Be it mergers, acquisition or private equity
transactions, due diligence is a complex process that requires hours of
professional work across different parties. One of the activities here is the
redaction of confidential information from certain documents that can at times be
high-cost, expensive manual work. NLP and Text Analytics techniques such as
Named Entity Recognition, semantic similarity and image processing now offer a
strong set of applications in this context. Confidential information, terms,
and topics such as commercially sensitive information, sensitive personal data
(GDPR) and others can be intelligently masked and unmasked, depending on the
audience viewing the data. Below is an illustration on the possible steps to
take for developing a similar model.
Vector representations of the sentences, with models
like BERT, GPT and others, facilitate search within documents based on
similarity. This means that functionalities such as finding similar words or
expressions within a document to redact are also possible.
Contract analytics are another group of use cases powered
by Text Analytics and NLP used in due diligence processes. Be it for a buyer
reviewing existing contracts of a potential target, or the target carrying out
an initial diagnostic analysis, a streamlined and automated process can save
significant amount of manual work. Contracts keep information in an
unstructured manner but include important clauses that may fall under the radar
during the due diligence process. The thorough analysis of the contracts naturally
involves exorbitant legal fees. Semantic search, intelligent tagging, and summarization
of important information from contracts can significantly reduce the number of
hours lawyers need in analyzing the contracts and thus save costs.
Research and analysis of the investment climate
have already seen several uses of analyzing unstructured data for research or
investment management purposes. Along with text data,
there are also use cases available for leveraging foot traffic information, online
search, and other similar data for more refined forecasts. During the start of
the pandemic, many of the analysts were slow in updating their numeric targets
for the companies although many of the companies have withdrawn their earnings guidance.
During the financial crisis a similar phenomenon was observed. During the times
of uncertainty numeric indicators are updated less often whereas the current
challenges and the existing investment climate is discussed at length in text
form. Investment managers that leverage this data in decision making processes
have a holistic view on the current climate. Normally a human analyst would
review these reports. But in a fast-paced environment manual review of all
documents is not feasible. As such NLP and Text Analytics techniques can help
in doing an initial screening of the reports and highlighting the areas to have
a look at.
Nowcasting and Central Bank research
During the significant volatility driven by the
lockdowns and the pandemic, central banks were struggling in adjusting their
monetary policy. Conventionally used figures for monetary policy committees
such as GDP, consumer sentiment indexes, inflation numbers and others are backward
looking. Major central banks have thus seen greater use of so called
nowcasting. The need for immediate data in stressed scenarios is rather
pressing. As such nowcasting, by leveraging more high frequency data such as
text and newspaper articles, provides an immediate insight into the market
environment. Nowcasting is not only used in making monetary policy decisions
but also gauging the effect of the decisions made and maneuvering where
necessary. The trick is constructing the dataset into a timeseries that can be
aligned with corresponding numeric indicators. Researchers from
the Norwegian Central bank use LDA (Latent Drichlet Allocation) for this
purpose to extract topics from news sources and forecast US GDP, investment growth
3 Recommendations to smaller and medium sized FIs
Alternative data sources such as text demonstrate a
wide set of advantages over conventional numeric indicators. These are more frequent,
tell a wider narrative about the economy, incorporate qualitative information,
and speak about expectations. With this in mind, there are 3 things that
smaller and medium sized FIs should do to gain a competitive advantage over the
larger incumbents in the market.
- Technology and business teams inside the
FIs should do regular workshops together to identify business focused use cases
for leveraging AI, ML and NLP. Speaking to consultancies helps in identifying
use cases given the wider industry footprint and the continuous research that
consulting firms do.
- Consider deploying no-code AI platforms to
empower business users to experiment. Its during experimentation that ideas
come to life. With closer collaboration with business focused teams, a much
stronger business case behind the technology initiatives can be made.
- Prefer finance industry specific over generic solutions that are
aimed at any industry. A sustainable competitive advantage can be achieved
when solutions leverage industry expertise and are configured to
individual organisational needs.