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Tag: Artificial Intelligence and Machine Learning

With rampant use of artificial intelligence and machine learning, how are financial institutions dealiing with problems related to data bias and transparency?

Posted on by Imarticus Learning

The public and private sectors are increasingly turning to machine learning (ML) algorithms and artificial intelligence (AI) systems to automate every decision-making process, and financial institutions are no exception.

In addition to widespread use in the capital markets, artificial intelligence and machine learning are used in financial services to make insurance decisions, monitor user behavior, recruitments, fraud detection, credit referencing, and underwriting loans.

However, while AI and ML have brought innumerable benefits to financial institutions, they also have their share of woes in the form of data biases and transparency issues. The question is, how are financial institutions dealing with these problems?

Bias and Transparency in the AI Context

AI systems are powered by algorithms that “train” by reviewing massive datasets to ultimately identify patterns and make decisions based on the observations. Hence, these systems are no better than the fed data, resulting in unconscious data biases.

On the contrary, transparency in the context of AI refers to the ability to explain AI-based decisions. Given the increasingly complex findings and algorithms, ensuring transparency to different stakeholders is vital in the financial sector, both from compliance and business value perspectives.

Biases can occur in many ways. For example, bias due to incomplete data occurs when the AI system has been trained on data that is not representative of the population.

Likewise, the dataset could be biased towards previous decision-making processes, the programmer may introduce their own bias into codes, or business policies pertaining to AI decisions could be biased themselves. The bias of any form eventually leads to unfairness and inequities in financial services.

Dealing With AI Bias and Transparency

Although the use of AI and ML give rise to data bias and transparency issues, they have become indispensable for the functioning of financial services. So, the only course of action left to financial institutions is to adopt ways to get around the problems. Some of them are listed below:

  • Financial institutions and firms can have appropriate controls and monitoring tools to ensure that new data entering the pool is reliable and of high quality. 
  • In addition, some organizations have developed tools to determine if a potential AI solution is biased. 
  • When building AI systems, it is wise to gather a team with domain expertise, model development skills, data engineering capabilities, and commercial expertise. 
  • Organizations can undertake impact assessments of the AI solutions to ensure they are transparent and explainable, as well as determine how the AI-based decision-making process will impact customers. 
  • When engaging with AI technologies, financial services can apply safeguards to ensure that business outcomes are achieved, and customers’ interests are protected. 
  • Another way to minimize data biases is to be open on the user data, match and align data with the target segment, and set up review cycles with legal and statistical experts. 
  • Tracking mechanisms that allow one to track the decision-making mechanism of algorithms can be put in place to eliminate bias and ensure transparency as much as possible. 
  • Lastly, it is pertinent for institutions to document their approach to handling bias and review it after every stage of development and use of the algorithm.

What to Look for in an Artificial Intelligence Course?

If you want to learn AI and ML, there are several online courses you can choose from. An AI and ML certification course that makes you future-ready will have a robust curriculum covering critical concepts related to data science, machine learning, NLP, deep learning, and computer vision.

In addition, the program should offer in-depth experiential learning through hands-on involvement with real-world projects.

Why artificial intelligence and machine learning is the new blue print for the data science industry

Posted on by Imarticus Learning

Data science is a broad discipline concerning data systems and processes with the aim to maintain data sets and derive insights from them. On the other hand, Artificial Intelligence (AI) pertains to mathematical algorithms that can replicate human thought processes to understand complex relationships, plan for the future and make actionable decisions.

Machine Learning (ML), on the contrary, helps to implement AI by “training” computers to solve various tasks. Data science incorporates several areas of artificial intelligence and machine learning while primarily focusing on gaining insights from data.

But, how are these three fields related, and what is the impact of AI and ML in shaping the data science industry? Let’s find out.

Data Science, AI, and ML: Where Lies the Difference? 

Data science finds widespread use in several businesses to improve production processes, innovation of product design, and enable strategic planning. It involves techniques of mathematics, statistics, computer science, and even ML to extract knowledge from data and provide insights and decision paths. 

On the contrary, AI enables computers to observe their environment and make decisions based on what they observe. Some of the most widespread uses of AI include processing clinical data, creating chatbots and smart assistants, and financial planning. Add the machine learning component, and AI can enable computers to solve new problems such as classification and predictions.

The fields of data science, AI and ML overlap significantly and yet have subtle differences. In a nutshell, data science gives insights, AI produces actions, and ML facilitates predictions.

The Combined Effect of AI and ML on Data Science

Data science and data analytics have long been revolutionizing the business landscape. Companies that have mastered their use of data science and analytics aim to delve deeper into data to increase efficiency, boost their bottom lines and gain a competitive edge.

Thus, they are looking to incorporate AI and ML into their data infrastructure to achieve business goals. For instance, call centers have long been using conversation analytics software, platforms that leverage AI and ML to gain better data insights. 

Following are a few more examples to show how AI and ML combined with data science make a remarkable difference to organizations:

  • Conversational AI systems such as chatbots and smart assistants engage in highly interactive conversations with customers and users and capture actionable user insights in the process. 
  • Predictive analytics applications enable the analysis of dynamic datasets to make financial predictions, forecast business trends, customer behavior, etc. 
  • Hyper-personalization systems enable customized offerings to customers, such as product recommendations, targeted advertising, personalized medical care, and financial planning. 
  • Also, organizations can consistently respond to evolving threats, thanks to anomaly detection systems that leverage the potential of adaptive fraud detection and cybersecurity processes.

The business value of data science alone cannot be understated. However, integrating it with the tools and techniques of AI and ML has way more potential to produce actionable insights from the ever-expanding data pool. In conclusion, AI and ML have been impacting the data science industry for a long time and will continue to do so in the foreseeable future with even more ground-breaking innovations.

How to Learn AI?

Looking for an artificial intelligence course? There are several AI and ML courses available online with extensive coverage of data science, ML, NLP, deep learning, and computer vision. But before you settle on a course, ensure that the curriculum offers practical learning through real-world projects, has scope for ample industry exposure, and provides a globally recognized certification after course completion.

How Machine Learning Systems Can Streamline Healthcare Disbursement Setups?

Posted on by Imarticus Learning

The ripple effects of the COVID19 pandemic have been felt across industries at several levels. The healthcare industry wasn’t spared either, with essential healthcare workers moving to the frontlines to deal with the emergency. As a result, many organizations saw their back-end operations, such as appointment bookings and disbursement trackers, floundering.

However, there is a silver lining in this situation– it’s that technology has speedily been integrated into systems. Telehealth software has seen a surge in demand so as to prevent risks of exposure; healthcare disbursements are next on the list to be made easier.

Healthcare disbursements are traditionally tricky and convoluted processes; the pandemic has put further amounts of strain on the system and caused frustration, delays, and errors. However, machine learning in healthcare is a step forward in fixing disbursement delays.

Here’s how:

  • Moving from Checks to Digital Disbursements

A majority of disbursement systems around the world rely heavily on cheques and other outdated methods. However, this has become a point of friction at this time considering courier services have shut down and deliveries are very delayed. In such a scenario, the use of digital reimbursement options, bolstered by machine learning, is tempting.

Providers can facilitate faster payouts through DTC (direct-to-consumer) payments. By shifting the process online, providers will also be able to keep track of all patient and consumer data on one server. Machine learning can be used to pull up the relevant information, create automated disbursement setups, and ensure the consumer receives their disbursement digitally. The reduced reliance on paper payment processes will lessen the load on healthcare finance systems as well as get disbursements out to the right people in a flash.

  • Addressing Glitches in Systems

Several reports talk of misplaced cheques, incorrect deposit information, and several such kinks in telehealth and digital healthcare solutions being used today. Machine learning can be leveraged to iron out these kinks because, especially during a healthcare crisis, such errors can have a snowball effect on consumers and providers alike.

Providers who use machine learning systems to manage delays will be able to maintain strict records of past and future payouts. The system can be trained to collect the right deposit information as well as cross-verify with other records if required. The reliance on an automated system, in this case, equals to a lesser reliance on outdated methods of payout tracking.

  • Simplify User Experience

Claiming payouts and processing them can become a nightmarish experience for both patients and healthcare providers alike. Machine learning systems effectively reduce quite a number of manual steps which, in turn, saves time, money, and efforts. Machine learning can be leveraged to extract critical information from healthcare contracts, estimate how much is owed, and prepare the right documentation in time for a payout.

For patients, too, the process of claiming payouts become simpler. They will no longer have to fill out a myriad of forms and move from office to counter over days. Instead, by automating certain processes from the providers’ ends, patients can be called in only to verify details if necessary and to provide any other physical documentation the healthcare provider may need.

Conclusion

The healthcare industry will most likely see a surge in the adoption of machine learning and artificial intelligence. This will be across the board– from handling disbursements to automating admissions and discharges. Therefore, students who are interested in pursuing an artificial intelligence career would do well to explore this niche and develop the right skillset for it.

You can do this by enrolling in a machine learning course that focuses on the healthcare system, or take on related projects that could leverage your portfolio when it comes to it. The current strains on healthcare providers worldwide have exposed significant cracks in the system that machine learning could most likely fix.

Jobs of The Future: Artificial Intelligence and Machine Learning

Posted on by Imarticus Learning

COVID-19 has inverted the ways we lived. The jolts can be felt across workplaces, particularly where it has forced organizations to reduce activities, including leisure, restaurants, oil & gas, and airlines. Throughout COVID-19, the technology industry remains strong. The pandemic spurred technological innovation and enabled many to continue work despite lockdowns & other pandemic mitigation measures.

Benefits of AI?

  • Automation: AI gives a better understanding of machines to interpret a situation or perform necessary action. Tasks can be automated with minor human intervention through AI/ML. While automation takes place, the roles requiring human attention automatically become more productive with more time to focus on them.
  • Speed: AI is efficient in expediting much work when compared to humans. AI lets us complete tasks flexibly before deadlines. This reduces human labor & provides great speed & efficiency.
  • Accuracy: AI eliminates maximum chances of error. The machine always acts according to a fixed AI algorithm; there are fewer errors in every given scenario. In short, AI defines new limits of accuracy & precision with lesser risks.
  • Exploration: AI has helped to discover many new sites, for example, volcanic sites, ocean beds, etc. Humans being vulnerable to these sites, can’t reach and survive these scenarios. Robots are meant to go to these places and collect data.
  • Data Collection & Analysis: Data analytics is the future technology in today’s business world. Industries & businesses analyze valuable chunks of data & extract helpful information.

Applications of AI?

Artificial Intelligence and Machine Learning courses in IndiaAI is applicable in every conceivable field & recent advancements are increasing the relevance of AI in every sphere. Here are the top applications of AI:

  • Speech Recognition: AI allows us to convert spoken words into digital content. Speech recognition has various uses like voice-enabled messaging, content writing, voice-controlled remotes, & appliances. Speech recognition is also used for authorization & validation.
  • Natural Language Processing: NLP enables a machine to understand the human text. Virtual assistants like Siri, Google Assistant, Alexa, are all an example of chatbots working on the principle of NLP.
  • Stock Trading: There are AI platforms that allow automated stock trading. With the algorithms, these bots understand the fluctuations in the stock market & predict high-return stocks with more accuracy. The future scope of AI/ML in the finance sector is fuelled up due to the increasing craze for cryptocurrency.
  • Robots: Besides developing intelligent robots, AI has created robots that assist humans with routine tasks like cleaning, gardening, serving, etc.

Explore Careers in Artificial Intelligence with Imarticus Learning:

Freshers need to realize their competencies & acquire skills for AI roles with chances of upward mobility in career. The future scope of Artificial Intelligence is increasing due to new job roles & advancements in the AI sector. 42% of the IT workforce in India will require upskilling or reskilling by 2025. Imarticus Learning offers artificial intelligence and machine learning courses and machine learning certification courses to upskilling & and stay relevant.

The program builds a strong foundation of Data Science concepts. Industry experts will help you learn the practical implementation of Machine Learning, Deep Learning, and AI techniques through real-world projects from diverse industries. The 9-month extensive program will help you prepare for the Data Analyst, Data Scientist, Machine Learning Engineer, and AI Engineer roles.

This state-of-the-art Artificial Intelligence and Machine Learning Certification Course aim to let students learn machine learning & prepare for future jobs.

For further details, contact us through the Live Chat Support system or visit our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Hyderabad, Delhi, Gurgaon, and Ahmedabad.