Scientists and engineers believe that artificial intelligence and machine learning are potential solutions to all current and future problems faced by mankind. Various types of fraud have become one of the most burning and dangerous problems with devastating effects. This threat constantly evolves and can get many different shapes, from scam emails to deepfake videos of global leaders.
It is evident that human capabilities are not enough to fight this problem, so people need a more resourceful companion, like AI and ML technologies. They have shown great potential in detecting behavior patterns and specific features that are characteristic of fraud. The global investments in AI in fraud management are growing every year and are expected to exceed $57 billion in 2033.
Understanding Machine Learning and AI in Fraud Detection
Artificial intelligence is a general concept of simulating human thinking processes with computer technologies. Within this concept, there are several aspects, and machine learning is one of them. ML focuses on training AI based on given data sets without manual reprogramming. Usually, machine learning is performed using one of the three approaches: supervised, unsupervised, and reinforcement.
Deep learning is a subset of ML that focuses on improving automation by creating a neural network that resembles the structure of a human brain. As opposed to traditional ML approaches, deep learning algorithms can process unstructured information and improve their accuracy using such processes as backpropagation and gradient descent.
AI trained on specific historical data can become a powerful instrument for such complex tasks as fraud detection. Given that many schemes of online fraud rely on bots, the automated detection provided by AI and ML technologies is a very fitting solution with excellent capabilities of scaling and improving its performance.
Principal Fraud Types Detected by Artificial Intelligence
Defining even the approximate number of fraud schemes and tactics across all industries is challenging since new variants appear every day. Here are the three most popular categories of fraud associated with online activities.
Account Botting: Creating Fake Accounts and Bot Networks
Fake accounts are the modern-day plague of the Internet. Billions of fake profiles are automatically created and linked together, forming massive networks on popular social media and other relevant websites. For example, every quarter, Facebook removes from 1.3 to 2.2 billion fake profiles. Thanks to proactive measures, such as using AI for fraud detection, LinkedIn was able to detect and remove 44.7 million fake profiles during registration in the second half of 2022.
Online Payment Fraud: Card-Related Crimes
Bot networks are often used to perform brute-force attacks. One of the purposes of such attacks is to test stolen credit card information and determine whether it is still valid. This activity is usually characterized by large amounts of low-value orders. Machine learning fraud detection can identify and prevent such automated purchase attempts before they harm businesses and cardholders.
As another implementation of bot networks, account theft can be performed using brute-force methods similar to those described in the context of credit card fraud. Bots can try to input credentials from stolen databases or pick passwords based on the vocabularies of the most common variants, and so on. Again, AI solutions are innately good at identifying automated routines, and they can quickly adapt to new variants of such fraud.
Usually, the types of fraud mentioned above are the initial steps of complex schemes that involve other criminal activities, such as money laundering, investment scams, insurance scams, and so on.
How to Use Machine Learning for Fraud Detection?
Implementing Machine Learning for fraud detection can be broken down into a sequence of steps. To describe this process, we will use financial fraud as an example since it is one of the most popular types of scams encountered by businesses. However, with minimal adjustments, this scheme can be applied to fraud detection in other industries. For the sake of clarity, we will mostly refer to supervised learning as an example of using ML for fraud detection.
1. Providing Source Data
Teaching an AI involves feeding input information into a machine learning system to create a basic functional model that will be continuously improved. For supervised learning, source data must be pre-processed and structured. Specifically, it must be labeled as good when it refers to legitimate operations, and bad when it is related to known fraud. Unsupervised learning can discover patterns in unstructured information.
2. Extracting Relevant Parameters
In the context of financial fraud detection, the most common parameters of source data are:
number and frequency of successive transactions;
value of each transaction;
payment system and other information related to credit cards;
type of purchased product or service.
For example, when a popular fraud scheme involves ordering a specific product and then demanding a refund or chargeback due to alleged quality issues or failed deliveries, the known instances of such fraud will be broken down into individual parameters and analyzed as input data.
Another useful information for fraud detection with machine learning is user-related data, which includes:
IP address ranges;
detected use of VPN or other proxy services;
hardware IDs, such as MAC addresses;
various software-related data, such as name and version of OS, web browser, etc.
More input data requires more resources for processing but leads to a more accurate model capable of detecting more types of fraud.
3. Performing Rule Management
Source data analysis results in specific rules that define the fraud determination method. Basic rules involve one parameter: if it matches, an action will be marked as a potential fraud. For example, if a specific IP range is related to previous fraud, a rule will mark it as potentially unwanted. As a result, any IP address that belongs to the said range can be blocked automatically or flagged as a potential source of fraudulent activity.
However, most fraud detection algorithms use complex rules based on several parameters, which improves accuracy and reduces the probability of false positive triggering. Every parameter can be associated with a potential accuracy value and certain thresholds for triggering a relevant rule. In supervised learning, they can be reviewed and tuned by engineers after a predetermined number of cycles.
4. Training, Evaluating, and Tuning the Model
A fraud detection model can run multiple times on the relevant historical data with the chosen set of rules to make decisions. More runs or cycles improve the accuracy of the model and make it find more patterns. Testing the “real-world” performance of the model on previously unknown data is the vital stage of creating an AI-powered fraud detection solution. It allows engineers to review and finalize the rules and customize the model according to the specific features of the particular business and industry. Then, specialists must seamlessly integrate the ML model as a custom solution into the client’s digital infrastructure.
Benefits of Fraud Detection Using Machine Learning
Computers are excellent for processing and analyzing large amounts of information, so they are incredibly efficient in fraud detection. Here are a few advantages of using AI and ML for this purpose:
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Improved speed and efficiency. AI uses hardware capable of performing billions of instructions per second, which is far beyond human capabilities. With a sufficient amount of source data, artificial intelligence quickly learns to detect patterns characteristic of most frauds. This enables real-time detection and immediate response to fraud threats.
Less human involvement. For such a responsible task as fraud detection, the decisions made by AI have to be double-checked and finalized by experts. Still, computers provide significant time savings by going through all the hard work and presenting results for manual verification. This benefit allows managers to relieve staff from monotonous and tedious activities and direct specialists to more creative and skill-demanding tasks.
Better accuracy. Thanks to the ability to process large datasets, AI and ML solutions can reach conclusions with higher degrees of certainty. Further learning under the supervision of human experts additionally enhances fraud detection by increasing the probability rate and reducing false positives.
Higher learning potential. Again, AI’s excellence in processing information results in more advantages compared to human analysts. Data scientists just have to keep feeding new data to machine learning systems to enhance machine learning fraud detection models.
Cost savings. Since AI and ML greatly outperform the capabilities of human staff, these technologies present a much more cost-effective solution to the fraud problem. Additionally, they have excellent scalability to conform to the changing demands of the business. Compared to staff management, increasing the scale of an AI solution is cheaper and involves less hassle than finding and hiring a new specialist for fraud detection.
Round-the-clock availability. Unlike human staff, computers can work 24/7 and do not require holidays or weekends. Sure, the AI infrastructure is relatively complex and needs periodic maintenance, as well as hardware upgrades and software updates. However, these operations can be specifically planned and performed in stages to minimize downtime and ensure uninterrupted fraud detection with machine learning.
Though AI-based solutions require financial investments and the skills of many specialists to build and train learning models, all those efforts and resources eventually pay off. As AI learns and evolves, its beneficial effects become more powerful, and it gains more advantages over manual fraud detection. For example, artificial intelligence becomes more accurate and capable of functioning with less supervision.
Drawbacks of Machine Learning in Fraud Prevention
Though machine learning has proven its high efficiency in such demanding tasks as fraud detection, it also has several drawbacks. In some cases, they can have such a negative impact on the accuracy of conclusions made by Artificial Intelligence that manual analysis and human opinion would be preferable.
The complexity of implementation. Teaching an AI-based solution, just like teaching students, requires time, skills, and knowledge of experts. The unsupervised approach to ML that relies on deep learning algorithms has gained popularity over the years due to its autonomous nature and drastically improved accuracy. However, many experts still root for a supervised approach that requires constant human involvement.
Chance of malfunctioning and false triggering. When using AI-based analysis, there is always a possibility of a false positive result. In the context of fraud detection, this means that a legitimate activity is marked as illegal, which may result in restrictive actions against an innocent client. If an ML system is not aware that its decision is wrong, it can set off a chain reaction of further false detections that reduce accuracy and make AI choices increasingly unreliable.
Some models lack transparency and control. Depending on the type, ML models may be more or less automated and accurate. For example, black box models generally offer better automation and accuracy but do not offer many options for specialists to interpret their results and tweak their rules. On the contrary, white box models are more transparent and linear, so they provide clearly interpreted relations between input variables, fraud detection rules, and output results.
ML and AI Use Cases in Fraud Detection
Banking and Finance
JPMorgan Chase. Being among the global industry leaders, JPMC has been actively using artificial intelligence for years. The company has incorporated ML into its processes aimed atanomaly detection. Their AI-based security system that comprises deep learning algorithms and big data allows detecting malicious payloads like phishing emails or Trojan activities from both external and internal sources aimed at employees.
Healthcare
Elevance Health. This health insurance company, formerly known as Anthem Inc.,uses Google Cloud’s data analytics capabilities and AI-driven offerings to detect possible fraudulent claims. They use statistical models and algorithms for generating petabytes of synthetic data, including datasets of healthcare claims, medical histories, etc.
eCommerce
Shopifyuses and promotes a NoFraud system – an AI-driven decision engine that can analyze multiple data points on each transaction to eliminate fraudulent activity related to every step of order processing. The system examines numerous order details like IP address, device history, email longevity, social media, and others to identify the individual making a transaction and the possibility of fraudulence.
Wrapping Up
Frauds make their schemes smarter and more sophisticated every few months. So, to always be at least one step ahead of them, you need to engage artificial intelligence and machine learning in your fraud detection and prevention systems. The advantages these technologies bring to the fraud detection field are enormous – enhanced accuracy, total availability, unlimited learning potential, speed and efficiency, and a lot more. Despite a few drawbacks, using machine learning and artificial intelligence for fraud detection helps to save confidential information, money, time, and reputation for companies and their clients.
Intellectsoft is a custom software development company that has been delivering complex digital solutions for over 15 years. We offer a wide range of services and solutions, including enterprise artificial intelligence software that includes but is not limited to:
– Neural networks and deep learning;
– Custom machine learning models;
– Cloud-based AI-models;
– AI chatbots and applications;
– Face and voice recognition;
– Data analysis management;
– Raw data management;
– Data generation and augmentation, and more.
Over the years, we have delivered more than 600 bespoke solutions for businesses of all sizes – from early-stage startups to Fortune 500 enterprises from many countries across the globe. Last but not least, we have an engineering workforce in 21 countries, and almost 30% of our employees are females.
So, if you’re looking for a tech team that can build a solid fraud detection solution for your business using machine learning and artificial intelligence, don’t hesitate to contact us. As soon as our team gets your requirements, we will analyze your information and offer you the most effective solution or recommendations, depending on what you need. Just get in touch with us, and let’s make your product 100% fraudproof.
FAQ
Why do businesses use machine learning in fraud detection?
ML can provide an accurate and cost-efficient solution to most types of fraudulent activities encountered by businesses. Moreover, such a solution can evolve as it works, so it will be able to predict and detect new types of fraud.
Why are AI and ML better than human analysts in fraud detection?
They are available 24/7 and can process data and detect patterns much faster than human workers. Digital technologies are cost-effective and can detect and fend off massive automated attacks, such as fake account registration or other types of fraud-related activities performed by bot networks.
The hospitality industry depends a lot on keeping customers happy, but it’s getting harder to attract and keep them. The pandemic hit this industry hard, and now market competition is tougher than ever, with competitive offerings swaying customers. Hospitality businesses are willing to spend big to win customers back. The main questions for many are: how can we stay ahead of the competition, keep our customers, and increase revenue? Keep reading—we’ve got some answers.
Customers today have so many options that getting their attention is a real challenge. It’s not easy to make them choose your business over others. What worked 10 years ago doesn’t work the same way now. Back then, people often chose businesses based on reputation. But with new types of businesses (boutique hotels, craft cafes) popping up or big enterprises getting a new strategy, expectations have changed. The good news is these businesses have shown what works: great customer service. They focus on personalizing the experience, making things easy for customers, offering loyalty programs, and using smart technology.
In this article, we’ll explore these tech strategies and show you how they can help your business grow, and double the revenue. Keep reading to learn more!
The Importance of Customer Satisfaction in the Hospitality Industry
Customer satisfaction, which equals exceptional customer experience, is the backbone of the hospitality industry. It plays a central role in driving revenue and loyalty, as satisfied customers are more likely to return and recommend a hotel, restaurant, or other hospitality business to others. In fact, a study by the Harvard Business Review found that a 1% increase in customer satisfaction can lead to a 0.5% increase in revenue. Moreover, loyal customers are more likely to forgive mistakes and continue doing business with a company, even if they experience a negative encounter.
Let's quickly review the challenges the hospitality industry is facing right now so we can move on to the solutions and our case studies.
Challenges in Hospitality
As businesses navigate the changes in the hospitality industry, there are some key areas where a little improvement can go a long way. From improving customer service to embracing digital transformation and tackling labor shortages, we’ll take a closer look at today’s biggest challenges—and, more importantly, how technology can step in to help. Let’s get started!
Poor Customer Service
Even with strong and high-quality business offerings, poor customer service can drive potential customers and existing customers away. Common issues include:
Long wait times in customer service queues (it is worth remembering that our attention spans 8 seconds, and we live in a very busy world, so quick and efficient responses are more important than ever.)
Poorly trained customer service representatives
Lack of follow-up
Services not being done properly or quickly
Recall issues that require multiple service calls
These problems can frustrate customers. But, at Intellectsoft, we believe that the right approach, combined with technology, can address them effectively.
Digital Transformation Gaps
Approximately 50% of hotels are adopting new technologies, with 43% automating repetitive tasks and 39% upgrading existing systems. Source: workstaff.app
While many businesses are adopting digital solutions, some still struggle with outdated systems and fragmented data. Legacy companies often face chaos when trying to manage their data and create new systems or add features.
Some of the recent examples include clients coming to us to create AI solutions for their services while their data simply was not ready for the advanced tasks. We advise stepping back, communicating with a company objective, and working on keeping the data clean (centralized, structured, and segmented).
When we’re asked to create advanced systems using AI, we always emphasize the importance of organized data. To train AI and build such complicated systems that truly work, you need to start from the very beginning—collecting, storing, centralizing, and organizing it. We encourage our clients to align all departments to work internally on the data so we can create a unified digital system that delivers personalized experiences.
If you’re unsure where to start with your data, we can help. Collecting feedback at various touchpoints along the customer journey is crucial for gaining insights into customer satisfaction and loyalty. Book an IT consultation with us, and our experts will guide you in mapping out a path to get more from your data and build a system that works for your business and team.
Reskilling Staff
The pandemic hit hard in early 2020, especially for the hospitality and travel industries. Even five years later, many businesses are still feeling the impact. Recruiting and retaining skilled staff has become a major challenge post-pandemic.
Reskilling to address technology’s impact is of utmost importance, as about 40% of hotel General Managers place it among their top three workforce challenges.
We strongly believe that adopting the right technology can help. For example, creating an app to train your staff not only saves time but also ensures consistency in learning. AI assistants can also work alongside your team, helping with tasks like recruitment (providing industry insights, crafting emails) and staff training. Imagine having an app dedicated to making your team’s work easier and more efficient!
How Technology Tackles These Challenges and Supports a Customer-Centric Hospitality Culture
Automation, AI, and similar technologies may threaten many. However, the debate about replacing humans isn’t about that—it’s about cooperation and working hand in hand with technology to achieve the highest standards and establish brand-new “golden standards.” Technology is here to assist staff in listening to and collecting customer insights from data.
Let’s review some of our cases to see examples of how technology helps improve customer experience and supports customer service.
Examples of Intellectsoft projects
Property management systems (PMS)
We created a web platform and mobile app that makes managing daily tasks easier and offers extra services to improve residents’ experience. It gives a clear overview of daily activities and helps with managing units and users. Residents can easily book amenities, submit service requests, and report incidents. The platform also includes features for equipment management, key instructions, workflows, and custom permissions. This makes it easier for staff to stay on top of equipment and ensure residents have all the information they need. Plus, tools like announcements, quick votes, a forum, and a community calendar help build a connected and engaged community.
Guest Experience Management App
Our next app helped our client improve their guest experience by replacing printed marketing materials and the need for phone calls with a smart solution. Now, guests can easily order in-room dining, make reservations, and interact with staff— through a simple tablet in their room. The solution includes three parts: a custom in-room tablet with a variety of services, an admin panel for hotel staff and service providers, and a backend system that connects everything together. This approach makes things easier for guests and staff, improving both convenience and efficiency.
Existing Customers First: Building Loyalty with Personalized Touchpoints
Keeping existing customers is not just a strategy; it’s well-known that retaining existing customers is more cost-effective than acquiring new ones. In fact, research shows that it costs five times more to acquire a new customer than to retain an existing one. KPMG named customer retention as the number one revenue driver for the company.
Moreover, a 5% increase in customer retention can lead to a 25% to 95% increase in profits, showing the significant impact loyal customers can have on a business’s bottom line. Retention strategies, like personalized experiences and loyalty programs, can foster long-term relationships and generate recurring revenue, ultimately making them an invaluable part of any business model.
Personalization: How Technology Can Help?
Everyone loves to feel special—it’s part of being human. In hospitality, personalization has evolved from being a luxury to an essential standard. With the right technology to manage your data effectively, you can deliver those “wow” moments that guests won’t forget.
More than half of hotel chains are already using personalization features on their websites, booking platforms, and apps, and another 39% are planning to follow suit soon. They’re tailoring experiences to fit guest preferences perfectly.
Source: Deloitte
How does technology make it happen?
CRM Systems: They help you keep track of guest preferences, booking history, and special requests so you can offer services tailored just for them.
Loyalty Programs: Personalized rewards and offers show your guests that you value them, keeping them coming back for more.
AI Personalization: AI analyzes guest data to predict what they’ll love (from room upgrades to local recommendations) before they even ask.
IoT Gadgets: Smart room features like voice-activated assistants, or temperature controls adjust to your guests’ preferences, making their stay more comfortable.
With tech-powered personalization, you’re building loyalty and maximizing the lifetime value of every customer.
Our Case Study: Smart Hotel Management & Loyalty Program
For one of our clients, we created a smart room solution, complete with a custom IoT system. Guests can use a mobile app to control services, explore amenities, and find resort information-- with a special touch to their suite type. For the client’s entertainment business, we improved their legacy software by running a full IT and code check, fixing issues, and making the system better for customers.
Along with that, we developed a mobile app for the loyalty program, allowing businesses to effortlessly reward their customers and keep them engaged with exclusive benefits.
Our solutions not only helped the client with special experiences for guests; it also improved operations, cutting costs by eliminating inefficiencies. Here's what our experts shared:
"We transitioned away from ESB (Enterprise Service Bus) systems, which previously cost millions, and replaced them with more efficient, self-managed solutions. Similarly, we’ve implemented Device Farms that improved operations and saved resources."
Leveraging Software to Increase Upsell Opportunities
Here are some examples of tech tactics that will help your team sell more and introduce new offerings to existing customers, improve overall efficiency leaving your team grateful:
Dynamic Pricing Algorithms
For finance and marketing teams, manually calculating pricing by segment to introduce your clients can be incredibly challenging. AI-powered tools can strongly optimize these strategies by analyzing your database in detail.
Upselling via Apps
Boosting revenue in the hospitality business often comes down to personalized experiences. Imagine this: a guest books a room, and then your app suggests a spa package or a room upgrade at just the right moment. Later, it nudges them to book a dinner reservation or a guided tour designed to their preferences. With apps offering these personalized recommendations during and after bookings, you’re not just increasing your revenue per guest but also skyrocketing their experience. And you do it in a sustainable way, not by speculating, but by offering what your customers desire based on their previous experiences. It’s a win-win: seamless, helpful, and far from pushy.
Inventory and Resource Management Software
Making every resource count in your hospitality business is key. Inventory and resource management software allows you to effortlessly track everything from linens to room availability, ensuring nothing goes to waste. Having real-time insights to reduce overstock, avoid shortages, and optimize operations assists sustainably keep everything under control and know what you need to focus more. It helps maximize room occupancy by aligning bookings with available resources, ensuring every room and item is used effectively. This approach leads to smarter management, better guest experiences, and a noticeable boost to your bottom line.
Adopting these strategies not only optimizes your operations but also drives business growth. Ready to discover how technology can elevate your goals? We’re here to create a tailored solution for you.
Steps For Implementing a Customer Service Technology Solution
Improving customer service in hospitality doesn’t have to be a daunting task. With the right technology, you can smooth the path of your operations, speed up response times, and elevate the overall guest experience. Here’s where you can make it happen:
1. Understand Your Needs
Start by identifying the challenges your business is facing. Are you struggling to manage high volumes of inquiries, track customer feedback, or personalize the guest experience? Pinpointing your needs will guide you to the best solutions.
2. Pick the Right Tools
Choose technology that aligns with your goals. Look for solutions that are scalable, easy to use, and integrate effortlessly with your current systems. Options like CRM software, live chat tools, or a guest experience app can make a difference.
3. Equip Your Team
Technology is only as effective as the people using it. Train your staff to make the most of the new tools, so they can deliver exceptional service and resolve issues quickly. Confident, well-equipped staff creates happy, loyal customers.
4. Keep Improving
Once implemented, monitor how well the solution works. Are response times improving? Are customers more satisfied? Regular evaluations will help you fine-tune your approach and keep things running harmoniously.
Some Tech Ideas for Hospitality Businesses
CRM Software: Manage customer interactions and preferences.
Guest Experience Apps: Delight guests with personalized experiences right at their fingertips.
Amenity Management Systems: Simplify operations for everything from room service to facility bookings.
Live Chat Tools: Offer instant support and answers.
Social Media Management Tools: Keep customer interactions engaging and organized.
Integrating these strategies will not only improve your customer service but also deliver unforgettable customer satisfaction, loyalty, and, ultimately, your bottom line. Ready to take the next step? Book a consultation with our experts.
Conclusion
To wrap up everything we've discussed, customer satisfaction and experience are at the heart of hospitality. Retaining customers is more than just keeping them around—building strong, long-lasting relationships and consistently meeting their evolving needs. By understanding their pain points and offering personalized solutions, you can inspire loyalty and drive progress. With the right technology and continuous improvement, your business can stay ahead, let go of what no longer serves you, and keep growing.
At Intellectsoft, with over 17 years of experience in software development, our expert team is ready to help. We create everything from mobile apps to full-fledged portals and systems, leveraging the latest technologies like AI, Cloud, and Machine Learning. With deep experience in the hospitality industry, we’re here to craft personalized solutions that bring your business unforgettable customer experience and retain your valuable clients.
Ready to double your revenue and level up your customer retention strategy with personalized technology? Contact our experts, and we look forward to working hand in hand with you to build or revitalize the perfect app or system for you.
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