Four Use Cases for AI-powered Embedded Analytics
Machine learning has been making huge strides recently, and there are few better fits for AI than in analytics. Analytics these days are embedded in almost every application. Customers demand deeper insight into usage patterns and expect apps to give them this information.
In this article, we’ll be looking at four industries that have benefited immensely by adopting predictive analytics in their workflows.
Malicious actors are increasingly using AI to weaponize malware and bypass existing security systems. It makes sense to use AI to combat these attacks since manually reacting to intelligent algorithms might leave an organization a few steps behind the ball.
These days, organizations are increasingly turning to AI-powered cybersecurity systems that can detect malicious code and malware embedded in files before they can cause harm.
A study conducted in 2019 managed to use self-organizing incremental neural networks (SOINN) to detect malware. Researchers used SOINNs to convert binary files into visual representations. As a result, malware can be detected without having to run the code which protects the rest of the network.
While SOINN use represents a breakthrough technology that will make its way into products in the future, current solutions use AI in different ways.
Firms use self-learning algorithms to monitor network intrusions in real-time through anomaly detection, keyword matching, and statistical monitoring. AI is also used to analyze user behavior patterns to help prevent malicious insider attacks, which represent 20% of all 2019 cybersecurity incidents, according to Verizon.
Thanks to extensive research that is being conducted, AI usage in cybersecurity applications is poised to grow just in time as malicious actors turn to increasingly sophisticated methods of attack.
Online lending has steadily increased since the advent of the first peer to peer platforms in the mid-2000s and isn’t slowing down anytime soon. The global online lending marketplace is poised to grow to $11.6 billion by 2025, which represents a growth of 20.5% annually.
One of the reasons consumers prefer online lenders is faster approval times and less bureaucracy compared to traditional banks. Online lenders manage to deliver these benefits by utilizing AI to quickly analyze the credit-worthiness of borrowers by using alternative credit evaluation models.
Natural language processing (NLP) algorithms work very well in this field due to firms demanding a specific focus on credit-worthiness. Many firms use NLP to mine vast stores of data from alternative data sources. For example, NLP can analyze an applicant’s browsing patterns and correlate that to learned patterns of reliable borrowers. The analysis extends beyond browsing patterns.
AI algorithms can learn and build models based on a variety of factors such as social media usage, blogging contributions, geolocation data, and peer network membership. All of these data are gathered into elegantly embedded dashboards that provide real-time information to credit analysts. Analysts can run ad-hoc reports on these data or rely on parameterized reports to arrive at credit decisions. These processes allow lending firms to serve previously underserved sections of society, such as students or those with poor credit.
Telehealth services have grown massively during this decade, and their usage amongst consumers has accelerated due to the COVID-19 pandemic. AI-powered chatbots trained in domain-specific knowledge use NLP to diagnose patient illnesses after a question and answer session.
Diagnostic teams are increasingly using AI to collect samples from patients and diagnose test results in conjunction with medical history to propose diagnoses. Human employees take the recommendations of these AI assistants into consideration before diagnosing illnesses. As a result, doctors have full access to patient medical histories and symptoms summarized in dashboards before they ever see them in person.
AI is also being used by biotech companies to form patient diagnoses by analyzing genome sequences and comparing them to learned data. These algorithms can analyze over 37,000 sequenced genomes to arrive at a final diagnosis. This represents a major upgrade over current diagnosis methods.
Pharmaceutical manufacturing companies are increasingly using AI to develop new medicines and to find new applications for old medicines. Algorithms are also being used to identify possible new patients amongst those previously thought invulnerable to certain diseases.
Financial services is a wide industry that presents many applications for AI. Institutional wealth management firms routinely use AI-powered dashboards to track real-time alerts on target acquisitions or the market in general. Prime brokers use AI algorithms to carry out market-making trades with minimal human intervention.
Private equity firms rely on disparate data sources when analyzing acquisition targets. Integrating these various data into a single application used to be tough. These days, not only do embedded analytics providers integrate disparate sources of data, but they also offer predictive analytics depending on user-defined criteria.
Portfolio-level analytics help managers predict the effect of volatility events and carry out rebalancing activities accordingly. All of this helps firms reduce their risk in the markets and make better decisions for their clients.
Embedded AI Is the Future
With analytics appearing everywhere, it’s only a matter of time till they become predictive and offer insight into future events instead of merely drawing conclusions from past patterns. AI use is bound to increase beyond the industries highlighted. These four industries point to the industry-changing power of AI.