Case Study

Financial Industry

Empowering companies big and small

In the finance industry, fraudsters are dynamic and don’t follow a pattern, making them difficult to spot. These cybercriminals nowadays make use of the latest technological advances to their advantage. They can bypass security checks, and any company can lose millions of dollars in a matter of time.

One way to trace fraudulent transactions is to analyze and detect unusual activities using data mining techniques. But, the biggest challenge that the finance industry faces is implementing real-time claims assessment and improving the accuracy of fraud detection. 

Use Case in Financial Industry

However, Machine learning (ML) has become a popular method of fraud detection in recent years. It has shifted the industry’s focus away from traditional rule-based systems towards machine learning-based solutions.

ML technology is already being used by some of the most prominent financial institutions to fight fraudsters. MasterCard, for example, integrated machine learning with AI to track and process variables such as transaction size, time, location of the device, and purchase data. 

However, for better detection of frauds and data breaches, carefully chosen machine learning models are crucial. 

And, for setting up and training these machine learning models, a good dataset is the key. 

That’s when UBIAI’s text annotation tool plays its part. It helps in providing the fraud detection ML models with annotated text and critical datasets.

UBIAI's Semantic Search for Financial Industries

Annotating documents with relevant concepts is known as a semantic annotation. Documents are enhanced with metadata, which refers to the content and concepts. It makes it easier to find, understand and reuse unstructured content.

UBIAI’s semantic annotation provides innovative data pieces that contain highly structured, informative notes that machines can refer to. Semantic annotation solutions are used extensively for content recommendation, risk analysis, content discovery, regulatory compliance detection, and many other purposes.

UBIAI offers semantic annotation that results in metadata. This metadata describes the document through references to concepts and entities that are mentioned in the text. These references link the content with the formal descriptions of the concepts in a knowledge graph. This metadata is typically represented by a collection of tags or annotations that enrich the content or specific fragments with identifiers for concepts.




UBIAI's Semantic Analysis to Detect Fake Claims & Frauds

Semantic analysis can be used to analyze both structured and unstructured data. This feature detects fake and falsified insurance claims and other frauds. It improves the processing of insurance claims and other financial data. Machine learning algorithms based on UBIAI’s datasets examine files created by clients, insurance agents, and police officers, looking for inconsistencies. These textual data files contain many hidden clues.

UBIAI's Text Classification to Detect Duplicate Claims & Overstating Repair Costs

UBIAI helps build intelligent machine learning algorithms that can detect duplicate claims or inconsistencies when it comes to car insurance claims. The problem is solved by classifying the data. It allows you to uncover hidden correlations between claim records, the behavior of repair service providers, clients, or insurance agents.

UBIAI tool is used to create data sets for semantic analysis and relation extraction from documents used for fraud detection, such as property documents, insurance claims, etc. Although data labeling can still be done manually, humans are unable to classify sophisticated fraud attempts based on their implicit similarities. Therefore, data scientists use unsupervised learning models to classify data items into clusters accounting for all hidden correlations. It allows data labeling to be more precise. Not only are the items labeled as fraud/non-fraud, but they also nuance various types of fraudulent activity.

UBIAI to Build ML Model Based on Contract Intelligence Repair Costs

Contract intelligence, a new approach in CLM, allows companies to dynamically analyze and interpret contracts within the context of the systems that support their business. Artificial intelligence is used to make sure every contract’s intent is fully realized. It includes the signing and initiation of the contract and the management of obligations and analytics. In addition, the system provides real-time, high-impact insights that allow companies to have unmatched access to the exact information they require whenever they need it.

Artificial intelligence can increase the power of contract intelligence from beginning to end. Machine learning allows the system to grow smarter and more efficiently as it absorbs more information. UBIAI recognizes contract language and attributes in order to create datasets for Machine Learning.

Organizations can achieve compliance across millions or even thousands of contracts with contract intelligence. This technology automatically monitors and applies smart rules to the entire contract portfolio of a company to minimize risk and take appropriate action when conditions change. In addition, it adapts to your business and absorbs new information, making it smarter and more efficient over time.

UBIAI helps in generating training data to train a model that automatically classify documents in the legal domain. The model can identify document relationships, provide version control, and enable searching for specific information within the documents or contracts. In addition, it can automatically extract critical attributes, deliverables and obligations, and clauses from the unstructured contract of different formats. It alerts you to hidden risks.

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If you’re looking for a tagging platform on NLP projects, UBIAI probably has all the features you need. lt supports many document types and multiple OCR engines. It’s super simple and powerful.

Ihsan S.

Machine Learning Engineer

It is very easy to work with. A couple of things that draw me to UBIAI are that it could accept data in a LOT of formats (even including PDFs!).

Jetson W.

Data Architect

I love this product as this provides amazing support for document classification and named entity recognition. The NLP tasks UI is very intuitively embedded into this product and it is really easy to use.

Jasmeet K.

Data Scientist