A new way of understanding data
A difficult context that requires adaptation
Intelligence is the ability to learn efficiently, to react appropriately to information, and to make rational decisions.
Faced with economic upheavals and the abundance of available, often erroneous, data, detecting weak signals is crucial to improving the prediction of increasingly automated credit decisions.
Weak signals: clues to a strong signal
When it comes to managing customer risk in B2B, companies need to draw on a wide range of information from their environment. This information can be weak, strong, total, partial or specific. Each piece of information is part of a whole, coming from diverse and heterogeneous sources, to give it substance.
The processing of this information incorporates the concepts of threshold and intensity, which allow us to determine the point at which a sequence of events becomes a reality and a strong signal for anticipated risk management decisions.
What role does AI play in detecting weak signals?
The contribution of algorithms
With a forecast of a strong increase in business failures in the coming months (catching up with the pre-crisis level of Covid-19), it is crucial to detect quickly the fragility of its customers and prospects in order to reduce the risks.
Algorithms based on artificial intelligence (AI) learning mechanisms can help with this understanding by identifying key elements of predictions using failure analysis techniques, such as similarity calculation and data partitioning.
The purpose of this exploration is to aggregate data with similar properties to project future statements based on established information.
The need for early and rapid identification
The analysis and understanding of weak signals underlines the importance of the processes of data collection, selection and transmission of information in a strategy for monitoring a population of companies. The processing of weak signals can thus give a considerable advantage to economic players capable of interpreting them and extracting value from them.
To anticipate a threat or seize an opportunity, this rapid processing capability fills the gaps left by predictive analysis models, which are based solely on past events.
In other words, early identification of significant information boosts the decision-making process and value chain. The challenge for companies is not so much to find information, but rather to know how to sort through the mass of information collected in real time to extract meaning.
Good information is dated and sourced, fresh, verified, processed, accurate, relatively concise and targeted in its distribution. It acquires meaning and value when the message it contains has been deciphered, providing certainty in a given context, such as credit decision-making.
Invaluable business expertise
After being detected, validated, and enriched, the weak signal requires an analysis and cross-checking methodology to be properly evaluated. This evaluation can be automated by using linguistic sentiment analysis to determine the relevance and meaning of a trend or utility.
To set up an automated weak signal detection system, it is necessary to have the appropriate skills and tools, supervised by an expert mastering artificial intelligence technologies, as well as a relevant data collection and analysis methodology.
With the addition of this new data, decision processes are improved. The key success factors for decision modeling and automation are therefore:
- Control and qualification of business sources in automated flow mode,
- Taking into consideration the users' uses and expectations to define an effective detection and automation strategy for weak signals,
- Advances in AI through the use of algorithms and techniques such as Machine Learning and Deep Learning, which rely on machine learning, reasoning and perception. Natural language processing (NLP) is also inspiring this evolution.
Weak signals abound in all activities, and neglecting them can entail risks. Ultimately, the challenge is to convert the mass of available data into relevant, meaningful and reliable information, in order to improve the quality of human decision-making.
