Retail Banking Analytics Solutions & Commercial Banking Analytics Solutions
Text Analytics for Banking & Financial Services
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Transcript of Text Analytics for Banking & Financial Services
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FinancialTextAnalysis
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"Since the earliesttime, finance has
always been acornerstone of
human culture" Simon wentch
From the days of barter to today’s
cryptocurrencies, finance has always been
associated with the generation of data, such as
banking transactions, credit, insurance, and
investment reports
Day-to-day operations in finance entail
producing and consuming large amounts of
unstructured text data from various sources.
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However, the manual approaches to dataprocessing have over time been reduced in
use and importance
Because of this text analysis, the demand has increased significantly in recent years.
The field of text mining is constantly evolving alongside artificial intelligence. The
analysis of large numbers of financial data is both a requirement and an advantage
for companies, governments, and the general public.
Nowadays people predict and manage risks by text analysis, by making decisions
based on factual data and keep their customers happy and overcome their
competitors.
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Applicationsof FinancialText analysis
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Finance forcorporations
It comprises an analysis of all financial
and investment reports and a
sustainability assessment to detect
fraud.
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Financialforecasting
Text analysis contributes to stock
market prediction and forecasting. This
enables those involved to make
decisions based on facts rather than
pure speculation.
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Bankingoperations
Applications such as Money laundering
and risk management are used for text
analysis by financial managers.
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Challengesfor FinancialText Analysis
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1. Analysis can never achieve full accuracy due to theinvolvement of confidential data
2. Text analysis models lack a well-defined understandingof financial jargon.
3. Financial data is highly unstructured and redundant innature.
4. There are no dynamic text analysis models designedspecifically for financial operations.
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Text analysisModels forFinance
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TopiclabelingAnalyzing text data to identify
emerging topics in order to
identify rising and falling
financial market trends.
https://www.bytesview.com/topic-labeling
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SentimentAnalysisAnalyze feedback from your
customers extracted from multiple
sources and identify the sentiments
of the market towards a brand
market reputation. This helps in the
prediction of stock market trends.
https://www.bytesview.com/topic-labeling
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FeatureExtractionBanking transactions necessitate a
significant amount of textual data
processing. Feature extraction is a
technique for identifying and
structuring documents from a variety of
sources.
https://www.bytesview.com/topic-labeling
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EntityExtractionRecognize entities from
unstructured text and documents.
You can use it to extract valuable
financial insights from text data or
to keep track of your competitors.
https://www.bytesview.com/topic-labeling
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SemanticSimilaritiesComparing all financial products and
solutions to see how similar they are.
Identify similar data and use the tool to
avoid financial report duplication.
https://www.bytesview.com/topic-labeling
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Advanced text analysis solutionssuch as BytesView will allow you to
analyze volumes of financialunstructured text data from a
variety of sources
https://www.bytesview.com/industry/financial-serviceshttps://www.bytesview.com/industry/financial-services