How AI is combating deceptive emails!

How is AI combating deceptive emails blog cover image

Phishing emails are a constant threat. These emails, disguised as legitimate messages from banks, colleagues, or other trusted sources, aim to trick you into clicking malicious links or divulging sensitive information. But fear not, for Artificial Intelligence (AI) has emerged as a powerful tool in the fight against deceptive emails, acting as your own personal email detective. 

So, how does AI play this critical role? Let’s explore its impressive toolbox: 

  • Language Analysis Powerhouse: AI leverages Natural Language Processing (NLP) to dissect email content. NLP algorithms can identify suspicious patterns like grammatical errors, unusual sentence structures, or a tone inconsistent with the supposed sender. Imagine receiving an email from your bank riddled with typos and demanding “URGENT ACTION REQUIRED!!!”. AI flags these red flags, making you think twice before clicking. 
  • Learning from a Mountain of Data: Security researchers train machine learning algorithms on vast datasets of known phishing emails. These datasets include the tell-tale signs of phishing attempts, from specific keywords and phrases to sender addresses that don’t quite match the real organization [1]. By continuously learning and evolving, AI can adapt to new phishing tactics as they emerge, keeping you a step ahead of cybercriminals. 
  • Dissecting the Deceptive Disguise: Phishing emails often employ tactics to appear legitimate, like forging sender addresses or mimicking the branding of a trusted organization. AI can analyze email headers and source information to identify inconsistencies. Additionally, AI can check URLs embedded within emails against blacklists of known malicious websites. 
  • The Art of Anomaly Detection: AI can establish a baseline for your typical email behavior. This includes the senders you frequently receive emails from, the language used, and the types of attachments you normally encounter. Any significant deviations from this norm, like a sudden influx of emails from an unknown sender or a surge in emails with suspicious attachments, can trigger an AI alert, prompting further investigation. 

The benefits of AI-powered email security are undeniable: 

  • Real-Time Protection: AI can analyze emails instantaneously, quarantining suspicious messages before they ever reach your inbox. This significantly reduces the risk of you falling victim to a phishing attempt. 
  • Enhanced Accuracy: AI’s ability to analyze vast amounts of data and identify subtle patterns surpasses human capabilities. This leads to a significant improvement in the accuracy of detecting deceptive emails. 
  • Proactive Defense: AI can stay ahead of the curve by constantly learning about new phishing tactics. This proactive approach ensures your defenses are always adapting to the ever-evolving threat landscape. 

However, AI is a powerful tool, not a flawless solution. Here are some limitations to consider: 

  • Evolving Threats: As AI gets better at detecting phishing attempts, cybercriminals become more sophisticated in their tactics. Constant vigilance and ongoing AI development are crucial to stay ahead. 
  • Data Dependence: The effectiveness of AI relies heavily on the quality and quantity of data it’s trained on. Regularly updating training data with new phishing techniques is essential. 

The Future of AI and Email Security: 

The future of AI in email security is promising. As AI technology continues to develop, we can expect even more sophisticated email filtering and detection methods. Additionally, the integration of AI with other security solutions can create a comprehensive defense system against cyber threats. 

While AI plays a crucial role, it’s important to remember the human element. Always be cautious when opening emails, especially those with a sense of urgency or containing unexpected attachments. If something feels off, don’t hesitate to double-check the sender’s address, hover over links before clicking, and never share sensitive information via email unless you’re absolutely certain of the recipient’s legitimacy. 

With AI as your partner in crime detection, your inbox can become a safer space. The next time you check your email, take a moment to appreciate the silent guardian working behind the scenes to keep your data secure.

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The Reliability of Health Data During Crises & Emergencies

Following up on the issues discussed in our previous blog: The Present and the Future of Data Science in Healthcare. What You Don’t Know About Data Science in Healthcare? We are back with another one focusing primarily on the issue of reliability of data during uncertain and unfortunate times.

Today, we will dive deep by emphasizing on: healthcare system resilience, the importance of data during crisis and emergency, the right of access to public information vs maintaining data privacy, the recommendations to improve data use during that period. So, let’s find a calm and comfortable seat, and discuss these topics further…

Healthcare system resilience during a crisis and emergency:

The consequences of disaster can affect and put an extra burden on basic human needs such as food, shelter, health, and security in the short term. A long term effect is more visible on education and the economy.

Resilient health care infrastructure is foundational during natural disasters, and pandemics as these crises will increase pressures on the already available system. This resilient system is defined by Jun Rentschler et al. in his report (Frontline: Preparing Healthcare Systems for Shocks from Disasters to Pandemics) as: its capacity to maintain everyday quality of protecting human life and well-being while triumphing, managing and rapidly recovering from the current disaster. 

The importance of data during crisis and emergency:

  1. Pre-crisis: data can predict the magnitude of an emergency situation in the healthcare system, and prevents further damage.
  2. During crisis: 
    1. A data-driven healthcare system helps support the infrastructure, monitor the resources and reveal the gaps and overlaps between supply and demand during routine and surge health situations. 
    2. It suggests alternative or permanent plans and services and prioritizes the available finances, investments and human resources.
    3. It improves disease analysis, monitors progression, predicts a better action plan and enables effective response; for example, Matthew Robinson (2021) and his colleagues used a computer algorithm to anticipate the sickest patient who needs urgent treatment.
  3. Post-crisis: data can assess the efficacy of leadership and management plans that have been executed during the crisis, and can give insightful lessons that can be applied or avoided in the next disaster.

The balance between the right of access to public information vs maintaining data privacy during crisis and emergency:

UNESCO monitors and guarantees human right to access to the information within public authorities that includes the nature of the threat’s progression; the steps and decisions that are taken by the authorities to prevent this progression and protect the public; the emergency allocation of human resources, financial and equipment; and how to get more information.

During an emergency, using this information gives people security, autonomy and accountability when deciding for themselves or their family. Also, it should be accurate and based on evidence, not skewed, and anonymised as privacy protection is another human right.

However, the infrastructure of the data health system has the risk of cyberattacks either by penetrating into the personal patient’s data,or by disrupting health facilities administration systems.

The recommendations to improve data use during crisis and emergency:

Establishing an effective health data infrastructure both nationally and internationally that is based on diversity, ethics, shared values and interoperability. This system should manage the routine pre-crisis demands well before it can withstand the pressure during and post-crisis.

Investing in improving the skills of collecting, analysing and using data. Lessons from misuse of data, either by misinterpretation or violation of privacy and ethics, must be shared internationally.

Infodemic, defined by WHO as the spread of false or lack of information, should be well managed as it can lead to serious issues. It needs collaborative systemic study and evidence-based policies. The false information can be due to misinformation (unintentional) or disinformation (intentional). 

Cyberattacks are continuously emerging. In addition to our need for a resilient healthcare system, we need a resilient health data system that responds to threats routinely and constantly to protect patient data, healthcare system administration, and treatment devices.

It’s time you start taking HEALTHcare seriously.

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The next step:

It is not uncommon for 90% of a company’s or healthcare system’s data to go unused but here comes the tricky part: you never know when you will have to use a tiny part of that 90% in some of your analytics projects. From a purely business and medical perspective, the general rule of thumb is: when in doubt, collect data. Unfortunately, too many data projects fail at the very first step: reliable data collection which is the foundation of all further analytics efforts. Your customers may also ask for verification of certain data even after proper collection. And, you need to organize, audit and clean them before storage to ensure that your data analysis is based on high-quality data. 

You arrive successfully at the appropriate organization that offers you all: data collection, organization, verification, cleaning and maintenance services. If you want to save your time, money and energy, get in touch with us and book your free consultation.

What you don’t know about Data Science in healthcare?

The use of data science and artificial intelligence (AI) is increasing in the healthcare system: in the clinical setting (diagnosis to treatment to follow-up), in education, public health and bioresearch, and in administration and management. There is potential evolution medically and financially in healthcare with the good use of data science analysis. 

Therefore, it is crucial that we start a healthy discussion on issues such as the reliability of data during COVID & other medical emergencies, the recommendations of the experts in the field; the national academy of medicine, the gaps in data science in healthcare, and the future of data science. Let’s elaborate on these issues…

 

What is the reliability of data during uncertainty and COVID period?

Reliable data helps in the decision-making process. COVID crisis showed the importance of data science and artificial intelligence in healthcare. But what is equally important is how we collect and use this data. It’s dangerous medically and financially to lower the quality of research to get the most possible quantity. Sure, sometimes, we have to make decisions before getting the complete data. However, the usage of emergency data science can be fast but never rushed.

As Matthew Robinson (2021), an assistant professor of medicine in the Johns Hopkins School of Medicine, said in his article (Data-driven COVID-19 care A new algorithm created by Johns Hopkins scientists predicts which COVID-19 patients will become gravely ill): “IT makes it easier for clinicians to anticipate what will happen to patients and helps them focus on patients who are the sickest”. 

He and his colleagues at the Johns Hopkins University School of Medicine and the Bloomberg School of Public Health innovated SCARP, Severe COVID-19 Adaptive Risk Predictor, a computer algorithm program that alerts clinicians to those who need urgent treatment.

 

What are the recommendations of the National Academy of Medicine?

The National Academy of Medicine (NAM) is an American Institute of Medicine founded in 1970 and focuses on four strategic action domains—informatics, evidence, financing, and culture. 

To know more about NAM, visit their site: https://nam.edu/

Matheny et al (2019) wrote the NAM key recommendations to successfully execute data science in the healthcare system in his article (Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril):

  1. IT governance must manage AI applications and must set the standards of processing and implementing AI. They must enrol in healthcare system leadership and must analyze the financial benefits and risks of implementing AI. 
  2. The healthcare administrative leadership must define the short and long-term goals of applying AI to improve the healthcare system. They must be sceptical about the strength and advancement of research.
  3. The stakeholders such as governments, patients, consumers and the public must assess the transparency of the healthcare system, and assess the expectation, before operating AI. For example, assessing the intercultural resistance and workflow challenges.

What is the present gap in data science in healthcare?

Data science is a cornerstone in healthcare, but the problem that has been raised many times before, including in K. Rodolfa et al (2021)Taking Our Medicine: Standardizing Data Science Education With Practice at the Core is that there are very few programmed degrees that combine medical training with data science knowledge. It is important to understand that there is a difference between data science, epidemiology and biostatistics. Data scientists have more experience in computer science and informatics, while epidemiologists have a better working knowledge of study design and causal inference.

What is the future of data science in healthcare?

Focusing on training young students in the healthcare industry with specialized health data science degrees can be the current hope.

Over the years, institutions have been innovating to bridge the current gap, and one such developed program that integrates computer science, bioscience, and bioinformatics is innovated by Johns Hopkins University the Krieger School of Arts and Sciences and the Whiting School of Engineering.

Another area to improve in health data science is to create a trusted, flexible rapid response network [E.Kolaczyk et al (2021)]. This network will target three areas:

  1. People: the team consists of data scientists, policymakers, emergency leaders and community stakeholders who work together on the same goal.
  2. Science: the rapid response science should be based on standards, ethics and coordination
  3. Translation: the results of data science must be translated to policies and guidelines with an action plan.

It’s time you start taking HEALTHcare seriously.

Let's talk

 

 

 

What is the take-home action and solution? 

If you want to know more or have a new idea that needs planning to be implemented, get in touch with us by visiting our Healthcare Analytics page and book your free consultation.

Finance Analytics using Microsoft PowerBI

Finance Analytics using
Microsoft PowerBI

Oversee and keep track of your business’
finances with interactive analytics and key statistics.

Finance Analytics using
Microsoft PowerBI

Oversee and keep track of your business’
finances with interactive analytics and key statistics

Every day, businesses all over the world generate profound amounts of financial data. As we approach Industry 4.0 where data is coined as the new oil, what companies ultimately do with these huge volumes of data sets them apart from the rest. Having the fundamental financial awareness and proper tools to uncover the right information is paramount for a firm to function and expand.

The Finance department is essential for any business out there. Being financially aware and constantly improving money management strategies are pivotal for an enterprise to prosper. However, keeping track of the revenues and expenditures of every single department of a large firm may come off as a daunting task due to the sheer amount of workload and data that the Finance department may shoulder.

 

What are some Current Trends of Finance departments and companies?

The role of the Finance team is ever-changing and dynamic. In these times, most companies rely on Financial Data to plan for the future. Coerced by the pressure to constantly evolve, Finance departments and companies have been continually investing in fresh Artificial Intelligence technologies, as well as modern Data and Analytics tools in hopes of saving time and boosting productivity. However, a large majority of these tools and technologies are unable to process and generate larger volumes of data, causing Finance departments to run into a wall in the long run.

So what can we do with all this data? 

With Microsoft PowerBI, we can generate informative reports and interactive dashboards from large amounts of data to provide businesses with an all-in-one, concise overview of their financial status. Data of revenues, budgets, balance by departments, and much more can be presented neatly on these dashboards, and actionable insights can be acquired from them conveniently. By aiding businesses in understanding the top and bottom-line performances, Financial Analytics offers multifarious perspectives into organizational financial statuses and promotes profitability. 

On top of replacing physical labor with automation and improving the overall efficiency of the Financial department, PowerBI possesses the ability to take in and exhibit large amounts of data in the form of interactive dashboards and graphs without overloading the system. Information and actionable insights gained from the dashboards only increase in value over time, and Finance departments need not worry about plotting out graphs repeatedly in the future. 

With a wealth of financial data from the numerous departments throughout the organization, Finance teams are able to leverage the data collected and identify patterns to make relevant business predictions. The application of the freshly extracted knowledge and insights from the data sets can be translated into tangible business value, allowing businesses to make informed decisions regarding their expenditure and investment control. Essentially, Finance Analytics shapes business strategies through reliable, factual insight rather than intuition.

Finance analytical dashboards and charts – How do I go about doing it?

Below is a short video demo on how PowerBI, a growing Microsoft Business intelligence tool, has been used on a sample financial data, giving insights to the firm about their actual budget, expenditures, revenue gained, and much more. The demo walks through numerous reports and drills down to uncover deeper information, showcasing the flexibility and ease of usage of a tool like PowerBI as well.

Enhancing the reports

In our next case study, we will talk about how such reports can be extended to have elements of predictive analytics and machine learning which will further help businesses control costs and maximize revenue. Systems such as recommendation, financial forecasting, and others can enhance the usability and effectiveness of such reports.

Want to know more?

If you think this solution would be useful for your organization or you have a relevant use case or pain point you would like to tackle, get in touch with us today and we can help you and work together towards a solution!

HR Analytics using Microsoft PowerBI

HR Analytics using
Microsoft PowerBI

Analyze employees, attrition, diversity and
help your business take effective data-driven decisions

HR Analytics using
Microsoft PowerBI

Analyze employees, attrition, diversity and
help your business take effective data-driven decisions

What are the pain points faced by HR Professionals?

Even if you love your work as a human resource professional, every job has its “pain points”.

HR deals with many issues like:

  • Recruitment and Retention – finding and retaining talent is an ongoing challenge
  • Strategic Decisions (Time-Consuming) – work with unhappy employees
  • Monitor employee performance – especially critical for large companies with thousands of employees and multiple locations
  • Technology adoption – Workers who are used to old ways of doing things may resist change

How can Business Intelligence and Analytics help the HRs?

Business intelligence helps you take your business decisions more effectively with data and analysis. This creates the basis for success. It is an aid in all business areas, from growth to human resources to marketing, and helps transform several key processes.

Human resource departments fulfill several key functions within a company; such as hiring, training, organizing corporate events, and the not-so-pleasant business of firing. The HR manager’s role itself is to handle MANY things and demands unique solutions.

Business intelligence and analytics meet those needs in a variety of ways:

  • Business intelligence for…hiring
  • Business intelligence for… measuring success/performance
  • Business intelligence for… optimizing processes
  • Business intelligence for…cultural changes
  • Business intelligence for…high turnover rate

The main purpose of Human Resource management is to measure the work achievement of employees, their role in the services or business, and to analyze employee retention and attrition in the company.

All human resource reports and dashboards are persona-based, in the current context it explains why there is an increasing emphasis on finding and attracting the best talent. Information is spread by Powerful Views like Headcount summary, Actives & Separation Summary, Recruitment source, and many others.

Some common reports that HRs like to gain insights from

Headcount summary:

Headcount summary explains the present headcount of employees by location, gender, department, company, total employees by recruitment source.

Actives & Separation Summary:

Actives & Separation summary explains separation by region, active & separation by gender, active & separation by race, separation by performance score.

Recruitment source:

Recruitment source summary explains performance score by recruitment source, active & separation by recruitment source, recruitment source by gender, and so on.

Below is a short demo on how PowerBI, a growing Microsoft Business intelligence tool, has been used on a sample HR data, to give insights to the business about headcount, recruitment source, employee performance, and other metrics and indicators. The demo walks through multiple reports and drills down to give the organization detailed information. It also shows the ease of use of a tool like PowerBI with huge data, both for simple and complex reports.

Conclusion

HR analytics help HR teams set goals, measure success, and optimize processes so the company can focus on employee satisfaction. When used responsibly and effectively, HR analytics provide the insights companies need to tackle difficult challenges like lack of diversity or a high turnover rate.

Want to know more?

If you think this solution would be useful for your organization or you have a relevant use case or pain point you would like to tackle, get in touch with us today and we can help you and work together towards a solution!

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