Drug discovery covers the whole field of the drug development process, from identifying chemical compounds that could become useful drugs to the clinical trial phase. The drug discovery process is long and complicated, with every stage ripe for implementation of artificial intelligence models.
A store’s location directly impacts supply, profits, marketing, and almost every other aspect of store performance. Much can be learned from the performance of different types of retail stores in different neighborhoods, countries, or even climate areas. Therefore, retailers use geospatial data machine learning models to plan store locations and predict profits.
Hedge fund investments use several different strategies to achieve returns in both domestic and international markets. They are often aggressively managed and trade in land, real estate, stocks, derivatives, currencies, etc. markets.
The most lucrative hedge funds also have a diverse portfolio—which means investors need to analyze large amounts of data to make investment decisions.
Insurance claims management is the process of managing a claim from reception to settlement. The insurance claim process is particularly suited to machine learning solutions as much can be done to cut time and costs, leading to speedier resolution of claims to the satisfaction of both insurer and insured.
Insurance fraud can be committed by either the buyer or the seller of an insurance policy.
The seller may offer policies from non-existent companies, fail to submit premiums, and churn policies to create more commissions. The buyer exaggerate claims, falsify medical history, post-date policies, sell their policy to for cash when they are diagnosed with a terminal disease, or fake their death or kidnapping. We will focus on the buyer insurance fraud in this post.
Historically, insurers have relied on linear regression of a small number of risk factors, largely reported by the policy-holder on a trust basis, to determine an insurance premium. However, a good prediction model of individual future insurance costs is becoming a business essential as competition in the insurance industry and low customer switching costs have become key drivers for insurers to build a pricing structure which covers their incurred costs.
Everyone wants more leads, but the more we are able to generate, the harder it becomes to identify which of them are actually worth the time and effort spent in order to try to convert them. Lead scoring models let you automatically rank your leads in order of the perceived value each lead represents to your company. Resources for marketing and sales can then be distributed by the priority determined by lead scoring.
Customer lifetime value (CLTV) is the expected profit that a single customer brings to a company over the course of their lifetime. CLTV represents a shift in emphasis from quarterly or annual profits to the long-term relationships with customers.
Most if not all companies and organizations seek to establish themselves in social media platforms such as Facebook, Twitter, and Instagram in addition to their official website. Online and social media performance tracking is meant to analyze the effectiveness of a company’s social media presence. Are they generating new leads? Are they promoting brand awareness? These questions are important to assess the efficiency of the use of these social media platforms.
An online recommendation engine is software that analyzes available user data to generate suggestions for something the user may also be interested in. Such engines are used in an advertising capacity to promote products or services or for the purpose of recommending similar content to the content the user has consumed in the past, such as in streaming services and online shopping services.
Price optimization is the mathematical analysis by a company determines the response of potential buyers to different prices for its products and services. The aim is to meet a company’s objectives of maximizing profits and growing and retaining a customer base.
Product performance forecasting predicts future sales volumes—at what price, during which time, and in which market. It enables businesses to make informed decisions in both the short term and the long term. Nowadays, companies rely on machine learning software for product performance forecasting as these programs continuously improve, providing businesses with faster and more reliable estimates all the time.
Promotional planning is the process of optimizing marketing tools, strategies, and resources to promote a product or service to generate demand and meet set objectives. Artificial intelligence (AI) can be used to effectively plan promotional events, measuring their outcomes, and adjusting as necessary to achieve growth.
Property value and mortgage value assessment is the estimation of the future value of a particular asset for purchase, insurance, and more.
Unlike most other purchases, real estate tends to rise in value over time. This rise is influenced by economic and social trends, environmental conditions, and on governmental controls and regulations. These large-scale trends directly affect the general demand for property ownership, the scarcity of property for purchase, the utility of the property for potential owners, and the ease at which property rights can be transferred.
Remarketing campaigns show ads to people who have visited a business’s website or downloaded one of its apps. Remarketing identifies people who have shown interest in a company in order to prompt them to recall the business, increasing the odds of them converting.
Apartment rental prices are influenced by various factors. The aim of a house or apartment rent prediction model is to analyze the different features of an apartment and its surroundings to generate the most suitable price for rent.
Organizations must satisfy the unique demands of their customers. The good old days of mass marketing and predictions on limited mixture of products in one location are gone. Conversely, the use of artificial intelligence in inventory generates huge dividends to companies willing to reshape their supply chain orders.
This technological development results from the availability of massive amounts of real-time data now routinely generated by enterprise software systems and smart products. By collecting this data, organizing it, and interpreting it, artificial intelligence and machine learning have introduced an entirely new level of data processing leading to deeper business insights.
Nowadays, the abundance of data and the advances in Machine Learning and big data applications reduce the need for top-down segmentation of customers. Smart customer clustering based on many commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.
Algorithmic stock trading—aka “algo-trading”—uses machine learning algorithms to make stock market trades faster than a human could, calculating the best time, price, and amount to trade in an instant.
Case management is the optimization of customer care through the construction of a network of formal and informal activities, services, and supports.
Brand awareness is the degree to which a customer is able to recall and recognize your brand. Brand awareness is critical for promotion and marketing, especially for a young company. High brand awareness can result in a company becoming very popular or “trending.”
Fighting money laundering is a complicated task with substantial costs and risks, including—but not limited to—regulatory, reputational, and financial crime risks. Money laundering can be difficult to track, with many false alerts making detection even more challenging. But new technology, such as artificial intelligence (AI) and big data, can increase detection rates and keep your firm safe.
Fraud between companies can interrupt the flow of business and destroy their reputations and it is becoming increasingly difficult to identify and stop criminals from committing fraud: PYMNTS.com’s 2019 yearly report, “Securing B2B Payments,” relates that global markets lost $4.2 trillion in 2019 alone due to fraud. However, machine learning can identify fraud accurately before it has occurred.
In today’s global economy, supply chains include numerous partners, with services and sourcing managed across several organizations and in jurisdictions across the world. Corporations are increasing their use of third-party suppliers in the execution of key strategic imperatives and these third-party operations are becoming larger and more complicated as time goes on. Businesses should upgrade their risk management framework if they don’t want to miss potential profits and saved costs.
Many companies supply goods, loans, and services based on business and trade credit, either invoicing customers for payment at a later date or providing B2B loans. Business credit risk management assists companies with lending decisions based on a client’s financial health as well as other parameters that may indicate how likely they are to pay on time. Providing the right amount of credit will reduce the risk of late payments or defaults, which expose the vendor to financial risk.