Artificial Intelligence for Customer’s identity

When customers know what they want, companies must figure out what it is. However, according to Gartner, CMOs only invested 29% of their budgets in new technologies to meet their needs.

Probably marketing specialists do not have the appropriate technology or the technology they have isn´t enough. However, often marketing departments do not have full knowledge of the capabilities of the technologies they have paid for and consequently, do not take advantage of them.

To guarantee a high return of the investment for the business (ROI) the marketing specialists should start by auditing their technological ecosystem in their organizations to finally set the best ROI strategy.

However, the assumption that having a specific technology will guarantee to make money, is a common mistake. According to a survey conducted by the consultant company McKinsey, the technological gap between leading companies and others staying behind is growing. This means companies that make data-driven decisions will make the difference as compared to the rest of companies that are still struggling for basic data analysis and technology.

For both, innovating companies and the ones lagging behind, data analytics emerges as an opportunity to offer better insights, in other words, having a healthy data culture. Some will implement it and others will not. Therefore, some will obtain a higher ROI at the expense of companies that will lag behind as they will not implement a data-driven culture.

Once a healthy data-driven culture and appropriate technology are implemented, the time to listen to customers has come. As said before, the more consumer’s attention is captured, the more customer’s personalization will grow. The marketing departments invest around 14% of their budget to achieve the so long waited for personalization, very little in return for what it offers.

AI-based Customer identity

The need to know customers opens the adoption and use of AI not only at marketing scale but also at a broader and general level in the organization. The survey conducted by McKinsey Global Institute containing more than 3000 companies called “Artificial Intelligence: The next digital frontier?” shows that the first AI users are closer to the digital frontier. It´s precisely at this frontier where the leading companies in each sector are found. AI is present in all the groups, at the core business of the value chain, it is used to boost revenues, reduce costs, and has total executive management support. The companies that have not adopted AI technology at a certain scale or at the core business, are not sure of the revenues they could expect with such an investment.

Innovative companies know that existing and future customer personalization is achieved by customer’s knowledge. Here is where AI is introduced completely, and marketing specialists are trained to identify each one of them and personalize their actions.

One of the key elements of the customer’s identity techniques is that it allows us to characterize actionable concepts. These actionable concepts are the result of incorporating the business expert knowledge to the AI, also called Intelligent Observation Systems. The result is a set of concepts providing a broader customers perspective.

Therefore, customer’s identity techniques enable companies to obtain, for instance, better insights into consumer’s habits. Once they are identified, the different areas of the organization have enhanced the ability to make personalized offers leading to a greater benefit while delivering greater levels of customer satisfaction. For example, churn may be avoided once the customer’s satisfaction is analyzed. Additionally, price predictive models for certain products can be set.

Innovative companies are the ones able to respond to the classic questions: How do my customers behave? What value of information can I obtain from their habits? How can I improve the companies´ revenues? Partly, it´s due to their capacity to be always at the state of the art.



Supervised and unsupervised learning techniques for fraud detection.

Artificial intelligence is redefining fraud prevention techniques as it allows to obtain information based on experience. This information consists of transaction activities, behavior and trends. Before the use of artificial intelligence, the applied methods were based on rules that helped to analyze historical fraud patterns but could not prevent them. Although these models could identify fraud attempts, they did not provide information of the future.

Nevertheless, the technological sophistication of fraud crimes is higher and more precise and efficient attacks have grown during the last few years

As a result, leading companies dealing with potential fraud crimes, mainly in banking and insurance, must increase monitoring accuracy and acuity of customers potential risk for the institution. Decision-making on accepting or rejecting payments, limiting charges refund and reducing operational and reputational risks is much easier now.

Fraud prevention in the future will depend on a combination of supervised and unsupervised automatic learning techniques. Supervised automatic learning finds patters based on historical events, factors, trends, etc., and unsupervised automatic learning looks for relationships and variable links, a combination of both methodologies would help to prevent fraud in the following:

Detection in real time. The use of artificial intelligence enables the detection of attacks in real time, instead of weeks that usually takes to start receiving reverse requests of charges.

Thwarting the most sophisticated attempts of fraud. Fraud techniques get more and more sophisticated. Artificial intelligence would help to prevent and reduce these attacks.

Scoring in real time. Provide analysts a scoring for a better perspective to set the limits to maximize sales and minimize losses in real time.

Immediate transactions. Fraud prevention systems based on AI enable immediate transaction´s approval provided it is within the reverse charges threshold of the main debit and credit cards.

Reduction of false positives. False positives are reduced thanks to supervised and unsupervised automatic learning, whereas current techniques cannot efficiently detect them. Frequently, when a customer pays an unusual amount of money or from a new location, the card is blocked by the system as it interprets it wrongly as a suspicious activity. With artificial intelligence, it is possible to identify more precisely any change in customers expenditure habits.

Profitability in low margin products. AI has allowed insurance companies to continue their profitable business and attract new customers whose historical purchases are not part of the historical supervised learning of fraud systems

Supervised and unsupervised learning should be complemented with experts´ knowledge aiming at a mixed approach to focus their attention in more suspicious cases detected by AI.