helps ensure real-time automated transaction monitoring to ensure proper compliance with established rules and regulations.
Intelligent recommendations: AI can mine not just a consumer’s past online activity, credit score, and demographic profile, but also behavior patterns of similar customers, retail partners’ purchase histories — even the unstructured data of a customer’s social media posts or comments they’ve made in customer support chats, to deliver highly-targeted offers.
Insurance
Some in the industry think that factors unique to insurance — size, sales channel, product mix, and geography — are the fundamental cost drivers for insurers. However, a McKinsey survey notes that these factors account for just 19 percent of the differences in unit costs among property and casualty insurers and 46 percent among life insurers. The majority of costs are dependent on common business challenges, such as complexity, operating model, IT architecture, and performance management. AI can play a significant role in mitigating these costs.
Claims processing: Using NLP and ML, AI can process claims much faster than a human and then flag anomalies for manual review.
Fraud detection: The FBI estimates the annual cost of insurance fraud at more than $40 billion per year, adding $400 to $700 per year for the average U.S. family in the form of increased premiums. Using predictive analytics, AI can quickly process reams of documents and transactions to detect the subtle telltale markers that flag potential fraud or erratic account movements that could be the early signs of dementia.
Customer experience: Insurance carriers can use AI chatbots to improve the overall customer experience. Chatbots use natural-language patterns to communicate with consumers. They can answer questions, resolve complaints, and review claims.
Retail
The global economy continues to apply pressure to margins, but AI gives retailers many ways to push back.
Reduced customer churn: MBNA America found that a 5-percent reduction in customer churn can equate to a 125-percent increase in profitability. Predictive analytics can identify customers likely to leave as well as predicting the remedial actions most likely to be effective, such as targeted marketing and personalized promotions and incentives.
Improved customer experience: A 2014 McKinsey study notes that companies that improve their customer journey can see revenues increase by as much as 15 percent and lower costs by up to 20 percent. AI provides a deeper and contextual understanding of the customer as they interact with your brand. In particular, natural-language processing and predictive analytics provide a granular understanding of your customer regarding their product preferences, communication preferences, and which marketing campaigns are likely to resonate with each customer.
Optimized and flexible pricing: Predictive analytics enable a company to implement an optimized pricing strategy, pricing products according to a range of variables, such as channel, location, or time of year. The system creates highly accurate predictive models that study competitor prices, inventory levels, historic pricing patterns, and customer demand to ensure that pricing is correct for each situation, achieving up to 30 percent improvement in operating profit and increasing return on investment (ROI) up to 800 percent.
Personalized and targeted marketing: A 2016 Salesforce report found that 63 percent of millennials and 58 percent of Generation-X customers gladly share their data in return for personalized offers and discounts. Retailers are uniquely positioned to collect a range of data on individual customers, including preferences, buying history, and shopping patterns. Predictive analytics help personalize marketing and engagement strategies. A 2017 Segment study found that 49 percent of shoppers made impulse buys after receiving a personalized recommendation and 44 percent become repeat buyers after personalized experiences.
Improved inventory management: The days of overstocking inventory are quickly diminishing as retailers realize that optimized stock equals more profit. Predictive analytics gives retailers a better understanding of customer behavior to highlight areas of high demand, quickly identify sales trends, and optimize delivery so the right inventory goes to the right location. The results are streamlined supply chains, reduced storage costs, and expanded margins.
Legal
AI is tackling the mountain of paper that characterizes most legal proceedings by providing better and smarter insights from organizational data to detect compliance risks, predict case outcomes, analyze sentiment, identify useful documents, and gather business intelligence to make better-informed decisions. Through automation and the use of predictive analytics, these technologies have significantly helped reduce the time and costs associated with discovery.
A 2018 test pitted 20 lawyers with decades of experience against an AI agent three years into development and trained on tens of thousands of contracts. The task? Spot legal issues in five NDAs. The lawyers lost to the AI agent on time (average 92 minutes as opposed to 26 seconds) and accuracy (average of 85 percent as opposed to 94 percent).
In one case, a discovery team of three attorneys on a class-action lawsuit had 1.3 million documents to review. They used E-Discovery to code 97.7 percent of the 1.3 million documents as non-responsive, leaving fewer than 30,000 documents for the three-attorney team to review.
Human resources
Another bastion of paperwork, the HR department is a good candidate for streamlining processes using AI. In the 2018 “Littler Annual Employer Survey” of employers, the top three uses for AI were recruiting and hiring (49 percent), HR strategy and employee management (31 percent), and analyzing company policies and practices (24 percent).
As the average job opening attracts 250 resumes, the most immediate gains in efficiency are possible in recruiting and hiring. Scanning resumes into an applicant tracking system can reduce the time to screen from 15 minutes per resume to 1 minute. Natural-language processing and intent analysis go beyond keyword searches to find qualified candidates whose wording doesn’t exactly match the job posting. Virtual assistants interact with candidates to schedule meetings, an otherwise time-consuming and tedious task. By automating these and similar tasks, HR personnel have more time to focus on strategic tasks that require an interpersonal approach.
Supply chain
Globalization increases volatility in demand, lead times, costs, and regulatory hurdles, just to name a few factors. The announcement of a new trade tariff or a sudden flare-up of civil unrest can force quick adjustments and decisions. AI and data visualization techniques can accelerate the transition from reactive operations to predictive supply chain management and automated replenishment. It starts with recovering the value locked up in structured and unstructured data to convert a data swamp into a data lake to provide pervasive visibility of the current state of all assets across the entire organization and beyond to partners, customers, competitors, and even the impact of the weather on operations and fulfillment. It ends with streamlined processes, improved customer
