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smruthi33 edited this page Aug 29, 2018 · 18 revisions

Welcome to the Banking Risk Mitigation wiki!

Umbrella SI Journey

Leverage IBM Cloud, Watson services and Open source technologies to analyze client information and get early warning prior to any client investment.

Short Name

Client Network Banking

Short Description

Knowing your Client is an essential best practice because it is the foundation for all succeeding steps in the credit risk management process. To be successful, you must operate on pertinent, accurate, and timely information. However, Client network information is scattered across various sources. This pattern provides real-time information regarding a client, known as a client-network, all collated in a single place. It is targeted at Relationship managers at Banks who handle investments

Offering Type

Cognitive

Introduction

Relationship managers at banks handle client investments. One of the most important considerations in investing client money for a financial advisor is trying to assess the client’s risk tolerance. Risk can be defined in many analytical ways, but if you were to ask your clients their response would likely entail something along the lines of the risk of losing money. Investments are affected by happenings in the ecosystem or client network with events such as:

  • Management Change
  • Management Default
  • Share Price Deviations
  • Credit Rating
  • Strike

This code pattern takes real-time information from popular news sites, extracts the clients affected by it with the help of Watson Natural Language Understanding. This is acheived with the help of the following code patterns:

Finally, a flask application connects the algorithm to a UI which can be used by a user to prune down to the required information.

What makes this Code Pattern valuable:

The ability to process the tables in .docx files along with the free-floating text. And also the strategy on combining the results of the real-time analysis by Watson NLU and the results from the rules defined by a Subject matter expert or Domain expert.

Author

Code https://github.com/IBM/build-knowledge-base-with-domain-specific-documents/ Demo N/A Video

Overview In this Code pattern, we address the problem of extracting knowledge out of text and tables in word documents. A knowledge graph is built on the knowledge extracted making the knowledge queryable.

This pattern demonstrates a methodology to derive insights from the document containing raw text, information in tables with IBM Cloud, Watson services, Python package Mammoth, Python NLTK and IBM Data Science experience. The best of both worlds - training and rules-based approach is used to extract knowledge out of documents.

Flow

The unstructured text data from the .docx files(HTML tables and free floating text) that need to be analyzed and correlated is extracted from the documents using custom python code. The text is classified using NLU and also tagged using the code pattern - Extend Watson text classification The text is correlated with other text using the code pattern - Correlate documents The results are filtered using custom python code. The knowledge graph is constructed. Included components IBM Data Science Experience: Analyze data using RStudio, Jupyter, and Python in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.

IBM Cloud Object Storage: An IBM Cloud service that provides an unstructured cloud data store to build and deliver cost-effective apps and services with high reliability and fast speed to market.

Watson Natural Language Understanding: An IBM Cloud service that can analyze text to extract meta-data from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, semantic roles, using natural language understanding.

Featured technologies Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.

Natural Language Processing

Blog Title - Walkthrough on building a knowledge base by mining information stored in the documents.

One of the biggest challenge in the industry today is, how to make the machines understand the data in the documents just like a human can understand the context and intent of the document by reading it. The first step towards it is to somehow convert the unstructured information(free-floating text and tables text) to semi-structured format and then process it further. That’s where Graphs play a major role in giving shape and structure to the unstructured information present in the documents.

This code pattern has been designed to give a detailed description to developers who are keen on building the domain-specific Knowledge Graph. The Code Pattern covers and addresses all the aspects to it, right from the challenges that one can come across while building the knowledge graph and how to resolve them, how to fine-tuning this code pattern to meet their requirements. This Code pattern makes use of the Watson NLU, Extend Watson text Classification Code Pattern to augment the entities picked by [Watson NLU] (https://developer.ibm.com/code/patterns/extend-watson-text-classification/) , and correlate documents from different sources to augment the relations picked by Watson NLU. Basically, it makes the best of both the worlds- rule-based and dynamic Watson NLU. Then the results are filtered to meet the needs of that domain.

View the entire Knowledge graph Journey, including demos, code, and more!

Links (Watson NLU)[https://natural-language-understanding-demo.ng.bluemix.net/]

(Watson Studio)[https://dataplatform.ibm.com/]

(Python NLTK)[https://www.nltk.org/]

(Ultimate Guide to Understand & Implement Natural Language Processing)[https://www.analyticsvidhya.com/blog/2017/01/ultimate-guide-to-understand-implement-natural-language-processing-codes-in-python/]

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