AI in Market intelligence

Data insights provide business intelligence that is crucial for business to stay ahead of their competition. The term business intelligence (BI) is is associated with a set of technologies that help visualising the data, which is already a big help. Oftentimes it is enough to set simple rules and filters on the data to make business decisions. But AI/ML models go beyond BI and are able not only to visualise what is happening, but rather predict what can happen, spot anomalous (or interesting) situations, discover business opportunities. If only the smartest will make it, then AI/ML needs to be in this conversation.

Risk assessment

AI/ML models are widely used by risk assessment departments in banks and insurance companies, because rule-based software can’t possibly process such a big amount of data and be programmed for such a big amount of cases (rules). For example, a bank needs to assess if a transaction is fraudulent and have highly qualified (and paid) risk analysts that need to go through large databases of transactions and filter the suspicious ones. AI/ML using anomaly detection (unsupervised learning) can cluster large amounts of data and spot anomalies, so the risk analysts can spend their precious time reviewing only suspicious cases. Or a financial institution doing a credibility check on a potential new customer, they need to check their history and crosscheck information in different databases, and AI/ML models help finding patterns between different entities or physical persons.

Client scoring

Connected to risk assessment but a use case on its own, insurance companies and banks perform client scoring to be able to decide if and under what conditions those potential new clients can apply for a given service. For example, in the underscoring process of an insurance company, using AI/ML models they can have a much more complex picture of the customer, having access to more types of databases and information sources, and find relation in the data across all those different data points.

Customer feedback analysis

Customers are publishing their opinions and feedback of products in many places (app marketplaces, websites, Google search, in specialised portals, on social media, even on TV), and it’s just impossible to use rule-based systems to analyse such a big amount of data coming from so many different sources, and detect what is the sentiment about a given product. AI/ML models detect the sentiment in text and can make heat-maps or word maps where product managers can be notified of what features work and which don’t in any product, and then plan the engineering teams development according to what the market is demanding. Companies?

Market intelligence

With such an exponential growth of data points and sources, being up to date on market intelligence is crystal for companies. BI dashboards help to visualise data, but AI goes one step forward with predictive analytics. For example, let’s say a B2B e-commerce selling electrical equipment, and electronics to other electronic retailers. Margins are short, they are adding little value to their clients and being “commoditised”. But it turns out, they have visibility on what products are shops selling, so they can map the current demand, identify trends, and predict demand. From being an electrical equipment supplier with just price as the core value proposition, they can now offer market intelligence services. They can offer their clients which items are selling, which ones are expected to sell the most, which items are expected to have difficulties in the supply chain and make valuable market intelligence recommendations.?