Vector search: what are we talking about for Swiss banks
Searching for information in a database, on an engine, etc. is becoming increasingly precise thanks to technologies such as AI and Machine Learning. Our glossary provides answers to the questions you may have about vector search or semantic search.
What is vector search?
The vector search technique is based on Machine Learning (ML). It aims to understand the context and meaning of unstructured text or images. In particular, it is used to carry out semantic searches to identify similar or related data.
It is a technology that uses numerous algorithms to transform ‘unstructured’ content (a sentence or an image) into a digital representation in the form of a vector. It brings speed and relevance to processing.
What is the difference between semantic search and vector search?
Semantic search aims to make a search engine understand the meaning of phrases or expressions. It is a technique that provides answers to questions that are more complete and broader than those obtained by entering exact words. Semantic search takes into account the context of the search and the intention of the person querying the engine. Semantic search uses vector search technology to obtain these results.
What uses does semantic search have over keyword search?
Searching for information by keyword works on the principle of exact words. You can certainly carry out “long tail” type queries with an expression made up of several words. However, the results are limited.
Semantic search, on the other hand, works on the principle of NLP (Natural Language Processing) and automatic learning. Using large vectors, this type of search retrieves extended, semantically related information. Semantic search uses the KNN (K-nearest neighbour) or ANN (approximate nearest neighbour) principles.
What is the data similarity score in vector search?
The principle of the search engine is to identify similarities between the different data or documents in the database. In this case, the information has similar vectors. The aim of the similarity score is to measure the level of similarity. To carry out this analysis, vector search indexes the queries and data sources consulted. To do this, it uses the vector folding technique.
What are the advantages of vector search for compliance management in banks?
From our point of view, the combination of vector search with RegTech, thanks to Machine Learning, a form of AI, brings several practical benefits for compliance management.
Financial services staff working with regulation benefit from :
- the ability to search for information in multilingual mode simultaneously ;
- data extracted by the search engine based on semantic similarity (thanks to the similarity score) ;
- carry out a multi-modal search, not only in text, but also in images, audio or video ;
- formulate their query in a natural way without having to identify keywords.
Does e-Reg use vector or semantic search for financial regulation?
e-Reg, our RegTech solution, is dedicated to simplifying financial regulation for the Swiss financial services sector. There are a number of possible applications, both in banking establishments and for advisory functions. Our platform is based on the use of semantic search and vector data. You can see for yourself how powerful our model is, whatever the expression you’re looking for or the query you’re making, during a personalised demonstration.
👉To discover other definitions around RegTech, we suggest you return to our glossary table of contents.
👉If you’d like to find out more about easyReg, take a look at our RegTech solution.