Use Case Library
The use case library below contains customer stories that illustrate how businesses leverage the power of NEMESIS to solve the problems at credit union, banking, healthcare, manufacturing and more.
Use our library of use cases to explore real-world applications in your industry vertical, or look for applications in your specific field of interest.
Maintenance costs are estimated to range between 15% and 40% of total production costs. In the last three years roughly 82% of companies experienced at least one instance of unplanned downtime; in just the automotive industry alone, the average manufacturer loses $22,000 per minute when the production line stops. As a whole, this costs industrial manufacturers as much as $50 billion a year.
NEMESIS’s Value
Using NEMESIS, manufacturers can look ahead and determine future failures, maintenance schedule adjustments, and repairs that could be performed before the asset fails. The NEMESIS in-built Case Management System can help conduct preemptive investigations for safety compliance, corrective actions, and increased asset life.
Data
This case requires the use of maintenance historical data, including asset usage frequency, usage time, maintenance schedule details, repair date, etc.
Maintenance costs are estimated to range between 15% and 40% of total production costs. In the last three years roughly 82% of companies experienced at least one instance of unplanned downtime; in just the automotive industry alone, the average manufacturer loses $22,000 per minute when the production line stops. As a whole, this costs industrial manufacturers as much as $50 billion a year.
NEMESIS’s Value
Using NEMESIS, manufacturers can look ahead and determine future failures, maintenance schedule adjustments, and repairs that could be performed before the asset fails. The NEMESIS in-built Case Management System can help conduct preemptive investigations for safety compliance, corrective actions, and increased asset life.
Data
This case requires the use of maintenance historical data, including asset usage frequency, usage time, maintenance schedule details, repair date, etc.
Maintenance costs are estimated to range between 15% and 40% of total production costs. In the last three years roughly 82% of companies experienced at least one instance of unplanned downtime; in just the automotive industry alone, the average manufacturer loses $22,000 per minute when the production line stops. As a whole, this costs industrial manufacturers as much as $50 billion a year.
NEMESIS’s Value
Using NEMESIS, manufacturers can look ahead and determine future failures, maintenance schedule adjustments, and repairs that could be performed before the asset fails. The NEMESIS in-built Case Management System can help conduct preemptive investigations for safety compliance, corrective actions, and increased asset life.
Data
This case requires the use of maintenance historical data, including asset usage frequency, usage time, maintenance schedule details, repair date, etc.
Roughly 2.3 million Americans are victims of medical identity theft per year and have to pay an average of $13,450 in out-of-pocket expenses–two-thirds of those patients reported paying over 13,500. Medical identity theft is estimated to cost the healthcare industry over $30 billion a year.
You will most likely realize a victim of identity theft relatively quickly, but in most cases the victim of medical identity theft is not discovered until they go in for medical treatments or for a life threatening emergency procedure. Medical identity theft can sit and grow for years without being known. Without A.I. systems in place this becomes a major issue.
NEMESIS’s Value
Using NEMESIS, analysts can see claim details with trends and scores to identify whether this claim has a high probability of being a medical identitfy theft fraudulent case. Using the historical data of the medical insurance claims, analysts can easily build a predictive model to foresee the next move of the identity thefts.
Data
This case requires the use of historical claim data, including geo-location information, time parameters, policy information, claim type, claim amount, insured person description etc.
Roughly 2.3 million Americans are victims of medical identity theft per year and have to pay an average of $13,450 in out-of-pocket expenses–two-thirds of those patients reported paying over 13,500. Medical identity theft is estimated to cost the healthcare industry over $30 billion a year.
You will most likely realize a victim of identity theft relatively quickly, but in most cases the victim of medical identity theft is not discovered until they go in for medical treatments or for a life threatening emergency procedure. Medical identity theft can sit and grow for years without being known. Without A.I. systems in place this becomes a major issue.
NEMESIS’s Value
Using NEMESIS, analysts can see claim details with trends and scores to identify whether this claim has a high probability of being a medical identitfy theft fraudulent case. Using the historical data of the medical insurance claims, analysts can easily build a predictive model to foresee the next move of the identity thefts.
Data
This case requires the use of historical claim data, including geo-location information, time parameters, policy information, claim type, claim amount, insured person description etc.
Roughly 2.3 million Americans are victims of medical identity theft per year and have to pay an average of $13,450 in out-of-pocket expenses–two-thirds of those patients reported paying over 13,500. Medical identity theft is estimated to cost the healthcare industry over $30 billion a year.
You will most likely realize a victim of identity theft relatively quickly, but in most cases the victim of medical identity theft is not discovered until they go in for medical treatments or for a life threatening emergency procedure. Medical identity theft can sit and grow for years without being known. Without A.I. systems in place this becomes a major issue.
NEMESIS’s Value
Using NEMESIS, analysts can see claim details with trends and scores to identify whether this claim has a high probability of being a medical identitfy theft fraudulent case. Using the historical data of the medical insurance claims, analysts can easily build a predictive model to foresee the next move of the identity thefts.
Data
This case requires the use of historical claim data, including geo-location information, time parameters, policy information, claim type, claim amount, insured person description etc.
According to the California Department of Insurance, the Fraud Division received 15,112 suspected fraudulent claims (SFCs), assigned 532 new cases, made 201 arrests, and referred 317 cases to prosecuting authorities from 2020 to 2021. The potential loss amounted to $215,383,939. However, auto insurance fraud is underreported.
Insurance companies for auto are facing the headache of identifying the fraud out of the actual claims when it comes to automobile accidents. It requires analysts to dig deep into the client’s history and learn the behavior. It can also be acted by an identity theft or fraud scheme.
NEMESIS’s Value
Using NEMESIS, analysts can identify claims that have a high probability of being fraudulent so that SIU investigators can analyze and start investigations. Using a data set of automobile insurance claims, users can build a predictive model to score claims. Without leaving the platform, users can create a model results dashboard for investigators to see trends, scores, and claim details for case management.
Data
This case requires to use the 3rd-party data to link Personal Identification Information (PII) to claims. Use a dashboard to show cross-claim linkages as they appear with each new load of the 3rd-party data. The claim data includes policy information, insured person description, incident geo-location information, incident time, claim amount, auto model, etc.
According to the California Department of Insurance, the Fraud Division received 15,112 suspected fraudulent claims (SFCs), assigned 532 new cases, made 201 arrests, and referred 317 cases to prosecuting authorities from 2020 to 2021. The potential loss amounted to $215,383,939. However, auto insurance fraud is underreported.
Insurance companies for auto are facing the headache of identifying the fraud out of the actual claims when it comes to automobile accidents. It requires analysts to dig deep into the client’s history and learn the behavior. It can also be acted by an identity theft or fraud scheme.
NEMESIS’s Value
Using NEMESIS, analysts can identify claims that have a high probability of being fraudulent so that SIU investigators can analyze and start investigations. Using a data set of automobile insurance claims, users can build a predictive model to score claims. Without leaving the platform, users can create a model results dashboard for investigators to see trends, scores, and claim details for case management.
Data
This case requires to use the 3rd-party data to link Personal Identification Information (PII) to claims. Use a dashboard to show cross-claim linkages as they appear with each new load of the 3rd-party data. The claim data includes policy information, insured person description, incident geo-location information, incident time, claim amount, auto model, etc.
According to the California Department of Insurance, the Fraud Division received 15,112 suspected fraudulent claims (SFCs), assigned 532 new cases, made 201 arrests, and referred 317 cases to prosecuting authorities from 2020 to 2021. The potential loss amounted to $215,383,939. However, auto insurance fraud is underreported.
Insurance companies for auto are facing the headache of identifying the fraud out of the actual claims when it comes to automobile accidents. It requires analysts to dig deep into the client’s history and learn the behavior. It can also be acted by an identity theft or fraud scheme.
NEMESIS’s Value
Using NEMESIS, analysts can identify claims that have a high probability of being fraudulent so that SIU investigators can analyze and start investigations. Using a data set of automobile insurance claims, users can build a predictive model to score claims. Without leaving the platform, users can create a model results dashboard for investigators to see trends, scores, and claim details for case management.
Data
This case requires to use the 3rd-party data to link Personal Identification Information (PII) to claims. Use a dashboard to show cross-claim linkages as they appear with each new load of the 3rd-party data. The claim data includes policy information, insured person description, incident geo-location information, incident time, claim amount, auto model, etc.
Over the last five years, the number of identity thefts reported to the Federal Trade Commission has been steadily rising. In 2021, there were 22% more cases of identity theft reported than in 2020, racking up $1.2 billion in losses. Identity theft comes in many forms, including government benefits fraud, credit card fraud, loan fraud, bank account fraud, tax-related fraud, phone or utility fraud, etc.
Securing personal information can help prevent identity fraud. But that’s easier said than done. Banks, card issuers, and government need to take steps to combat identity fraud. Considering vast volumes and velocity, it would not be possible without automation, and AI/ML comes as a natural choice.
NEMESIS’s Value
NEMESIS’s Machine learning technology can make more precise predictive solutions, stopping identity thefts before the next act. Users can leverage NEMESIS’s integrated Case Management System to manage investigative and resolve fraudulent activities.
No matter how complex the data is, NEMESIS provides automated data cleansing and preparation with a simple drag-and-drop feature. Users can select from a range of built-in models for enabling and experimenting. Without leaving the platform, users can customize dashboards with a variety of charts and graphs.
Data
In classifying fraudulent activity, it is essential to provide labeled data, i.e., to create a variable indicating to which class (1 for fraudulent activity or 0 for otherwise) the given activity belongs t0. Other data include geo-location information, time parameters, channel, credit card limit, etc. Additional predictors are provided by banks, card companies, or government agencies.
Over the last five years, the number of identity thefts reported to the Federal Trade Commission has been steadily rising. In 2021, there were 22% more cases of identity theft reported than in 2020, racking up $1.2 billion in losses. Identity theft comes in many forms, including government benefits fraud, credit card fraud, loan fraud, bank account fraud, tax-related fraud, phone or utility fraud, etc.
Securing personal information can help prevent identity fraud. But that’s easier said than done. Banks, card issuers, and government need to take steps to combat identity fraud. Considering vast volumes and velocity, it would not be possible without automation, and AI/ML comes as a natural choice.
NEMESIS’s Value
NEMESIS’s Machine learning technology can make more precise predictive solutions, stopping identity thefts before the next act. Users can leverage NEMESIS’s integrated Case Management System to manage investigative and resolve fraudulent activities.
No matter how complex the data is, NEMESIS provides automated data cleansing and preparation with a simple drag-and-drop feature. Users can select from a range of built-in models for enabling and experimenting. Without leaving the platform, users can customize dashboards with a variety of charts and graphs.
Data
In classifying fraudulent activity, it is essential to provide labeled data, i.e., to create a variable indicating to which class (1 for fraudulent activity or 0 for otherwise) the given activity belongs t0. Other data include geo-location information, time parameters, channel, credit card limit, etc. Additional predictors are provided by banks, card companies, or government agencies.
Over the last five years, the number of identity thefts reported to the Federal Trade Commission has been steadily rising. In 2021, there were 22% more cases of identity theft reported than in 2020, racking up $1.2 billion in losses. Identity theft comes in many forms, including government benefits fraud, credit card fraud, loan fraud, bank account fraud, tax-related fraud, phone or utility fraud, etc.
Securing personal information can help prevent identity fraud. But that’s easier said than done. Banks, card issuers, and government need to take steps to combat identity fraud. Considering vast volumes and velocity, it would not be possible without automation, and AI/ML comes as a natural choice.
NEMESIS’s Value
NEMESIS’s Machine learning technology can make more precise predictive solutions, stopping identity thefts before the next act. Users can leverage NEMESIS’s integrated Case Management System to manage investigative and resolve fraudulent activities.
No matter how complex the data is, NEMESIS provides automated data cleansing and preparation with a simple drag-and-drop feature. Users can select from a range of built-in models for enabling and experimenting. Without leaving the platform, users can customize dashboards with a variety of charts and graphs.
Data
In classifying fraudulent activity, it is essential to provide labeled data, i.e., to create a variable indicating to which class (1 for fraudulent activity or 0 for otherwise) the given activity belongs t0. Other data include geo-location information, time parameters, channel, credit card limit, etc. Additional predictors are provided by banks, card companies, or government agencies.