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Pricing Datasets

Business is all about making money, and that includes the business of transferring money. Money transfer companies (MTCs) or international remittances companies provide multiple options for customers (individuals and companies) to remit income and other expenses nationally and internationally.

Pricing datasets for foreign exchange companies

MTCs provide online and offline payment solutions. Offline solutions refer to physical stores located across cities internationally. Using these stores, customers can provide physical cash and request their delivery in another currency to a store closest to the recipient. Offline stores are the most traditional options for overseas money transfer. Such stores are generally located in most cities across the world, especially in airports. Online payment solutions include online money transfer options such as bank transfer, credit/debit card, sofort, ACH direct debit, etc. This is an easier and more convenient alternative for customers who are savvy with internet banking options.

So, how do such businesses make their money? 

Foreign exchange companies make their money from the charges levied on each transaction. Transfer fees represent a bulk of a money transfer company’s profits, and charging such fees allows them to increase their bottom line. 

Pricing datasets provide incredible competitive advantage

The transfer fees are determined based on several important money transfer parameters, the primary ones being transfer amount, transfer speed, and transfer rate. 

  • Transfer account – The amount of money transferred in a single transaction
  • Transfer speed – The time taken in seconds, hours, or days, for the money to reach the recipient.
  • Transfer rate – The exchange rate for the selected send and receive currencies

Competitors across the money transfer space function within the same parameters, thus making it tricky to stand out. Some businesses are focusing more on offline stores while some others are directing their efforts into advanced online payment solutions. Irrespective of such business plans, every money transfer company can utilize competitor intelligence to optimize its parameters and increase revenue. 

Competitor Intelligence and its advantages

Investopedia defines competitor intelligence as the ability to gather, analyze, and use information collected on competitors, customers, and other market factors that contribute to a business’s competitive advantage.

In the money transfer space, access to competitor intelligence such as their transfer fees levied for selected send amounts, payment options, and delivery speed can be analyzed to understand where such businesses are profiting. Using such numbers, you can optimize your offerings, thus improving customer satisfaction and increasing your bottom line.

Pricing datasets for money transfer companies

Competitor intelligence in the form of pricing datasets allows you to track your competition, study competitor reaction to pricing changes, and explore untapped markets. It also helps you understand which aspect of your business operations needs to be improved to stay on top of the foreign exchange game!

Uses of competitor intelligence

Some of the popular use cases include:

  • Identifying competitors that offer the best deals to their customers
  • Fees charged by competitors across all currency corridors
  • The total premium charged by each competitor on all currency pairs
  • Time to disbursement vs premium charged

Bridged offers competitor intelligence solutions to money transfer companies looking to conquer the forex market. Click here to access our free pricing datasets, and let’s figure out a solution that gets you to the top!

AI in manufacturing can help bring the economy back on its feet

During the coronavirus outbreak, practicing social distancing and implementing lockdowns are the only viable solution to mitigate the virus’s spread. But, due to this, many industries are suffering, and world economies are facing trying times.

Reports suggest that the pandemic could cause the global economy to shrink by 1% in 2020. The statistic makes sense because the coronavirus has disrupted global supply chains and halted international trade activities. Industries such as hospitality and tourism have taken the biggest hit since world tourism is practically impossible during a pandemic. Large-scale events that cater to large gatherings (such as music festivals and movie screenings) have been postponed indefinitely. And, unless we develop a vaccine or achieve herd immunity, there are no signs of such events returning anytime soon. 

So, while the present reality isn’t showing many signs of economic promise, we don’t have any other choice but to put on our problem-solving hats and tackle this challenge. Emerging technologies such as AI can provide a much-needed boost to the economy. There are many challenges to confront due to the coronavirus pandemic. The one I’d like to focus on is the hurdles in manufacturing.

Problems faced by manufacturing

Manufacturing by nature doesn’t provide the luxury of working remotely, unlike industries such as Information Technology. And, the production of articles on a large scale using machinery cannot stop due to a pandemic. Most manufacturing setups are in developing countries, and halting operations will displace many workers and challenge their livelihoods. It will also create worldwide shortages which can cause further crises.

AI can get the manufacturing economy back on track

It’s clear that out of all industries affected, operations within manufacturing needs to return to normalcy as soon as possible. This can take place only if we can provide a hygienic and safe working environment that doesn’t put workers at risk of infection. Creating low contact processes and automating various factory floors could be strong first steps to get manufacturing up and running, and here’s where AI could provide saving grace, thus contributing to the economy:

AI in manufacturing

During times like these, AI can help manufacturing units by offering higher quality control, higher hygiene levels for workers, and transparency across teams with better communication. Also, AI-inspired robots can allow workers to maintain safe distances from each other, thus lowering the odds of contracting the virus. AI tools can give manufacturing the advantage it needs during this health crisis, and here’s how it will help get the economy back on track:

Contact-less machine control

Ideally, manufacturing floors will want to reduce the number of times workers come in contact with objects and shopfloor equipment pieces. AI-inspired gesture identifiers could take advantage of the human voice or human gestures. This is handy for switching on motors, initiating assembly line processes, and so on. For example, in a glass factory, workers could increase a furnace’s temperature by simply voicing a command or performing a hand gesture. Factories can train workers to use such new generation techniques and equipment. This will increase their skills and also reduce their chances of getting infected.

AI offers contact-less solutions for controlling machines

To develop such AI systems, we will need a variety of training data. Data that can teach systems to understand human communication and human languages. Such data will then be converted into system signals that instruct various mechanical processes. If it’s a machine that recognizes speech commands, the training data will comprise a large volume of audio files. Files that represent instructions, commands, and orders. And, if it’s meant to recognize hand gestures, it would use multiple annotated images of various hand signs that indicate a certain instruction or command. 

If implemented, this could allow workers to avoid touching surfaces and thus encourage a safe working environment.

Intelligent Automation

AI can automate many processes in factory assembly lines. Processes that presently use many workers at once. Automated processes will help factories stop workers from crowding in a single space. 

For example, factories need to review and screen their products before shipment. The reviewing and screening process is generally a manual one, in which multiple workers physically examine the same product. This increases the number of contact points, thus increasing the probability of infection spread. 

Automation will bring manufacturing back to normalcy

With the help of Artificial Intelligence, such review processes can be automated. AI developers can train computer vision systems to identify faulty products and packaging issues, after which they can alert concerned officers. Computer vision systems can also monitor the manufacturing floor. This will be especially useful for observing chemical reactions, heating/cooling operations, cutting/joining processes, and so on.

Leveraging data to handle supply/demand

Due to the coronavirus outbreak, manufacturing units cannot depend on historical figures to predict supply and demand. Demand for manufactured products has dwindled across industries. And, due to the world’s supply chain taking a hit, businesses can’t manufacture products with pre-pandemic efficiency.

AI can use manufacturing data to predict supply and demand

Here’s where businesses can leverage the power of data. Artificial Intelligence models can learn from present trends in supply and demand, to suggest manufacturing solutions. For example, an AI model can scan databases representing the demand for a certain product and determine how much of that product a factory should manufacture. Such models can also study the amount of raw material generated across the world. This helps determine whether a factory will be able to meet present-day demands.

Using such data, businesses can zero-in on target markets and ensure that their inventory allows them to cater to such markets. 

Conclusion

The coronavirus outbreak has revealed how Artificial Intelligence, Machine Learning, and Computer Vision are a blessing in disguise. Using such emerging technology, businesses can figure out novel methods to sail through these new and economically terrifying circumstances. Just like in manufacturing, we can develop AI systems for various industrial processes to ensure minimized contact without compromising on efficiency. With technology such as AI, we might just be able to maneuver through the pandemic successfully and bring the economy back on its feet.

Mistakes to avoid while training AI models

Artificial Intelligence involves the pursuit of human-ness in technology. Like teaching a child, AI development involves two things. The first being providing the study material (training data) and second being the learning method (Machine Learning, Deep Learning, etc.). 

For an ML model to perform well, it requires extensive training with a variety of training data. ML models consuming large amounts of training data allows them to understand diverse examples. And, a comprehensive training process increases the model’s odds of understanding and acting on the data at hand. 

The common problem faced by most developers is a misapplication of what was mentioned above. Simple strategy based problems have quick fixes, but those can seem distant or non-existent during the thick of the development phase. Here are some of the common mistakes developers make while training AI models, along with tips to avoid them:

Poor training data development

Training data is the juice that keeps AI/ML models functioning. Bad quality training data leads to bad quality results. It’s as simple as that. Bad quality is a broad term here, so allow me to break it down:

Lack of training data

ML models need multiple examples of a situation to understand how to tackle it. When there is a lack of training data, your model will not be able to identify real-world examples effectively. Analogous to how we learn, an AI model can function as required only if there has been a large number of examples to learn from (in this case, a large amount of training data). 

Unclean data

Having a large volume of training data is worth nothing if it’s quality is below par. Training data that’s riddled with errors will only confuse your ML model, which will render it unusable. Think about it, you can’t expect a student to learn if the reading material is filled with mistakes. 

Common examples of unclean data include inaccurately annotated images and videos, irrelevant data points, faulty conversational datasets (generally poor grammar and tonal issues).

Narrow data

To add the element of human experience to your AI/ML model, developers need to train it to understand specific rare scenarios and edge cases. Many AI developers falter here. They build algorithmically sound models, but they don’t train it to perform well when encountered with uncommon scenarios. For example, if an autonomous vehicle isn’t trained to tackle rare situations (such as protestors on the street, kids randomly running, etc.), the end result could be fatal.

The straightforward but tedious solution to solving this is exploring all scenarios your model might encounter, and feed datasets that represent all possible circumstances.

AI/ML model development snags

Even if the training data is sound, the AI/ML model at hand needs to be powerful enough to not only consume that data but reproduce usable results. Here are some common mistakes:

Machine Learning where it isn’t necessary

Yes, in many scenarios, companies decide to implement machine learning even when it doesn’t serve the purpose or serves it inadequately. In many situations, procedural logic does the job, so determine the need for ML implementation accordingly.

Performance analysis

Even if an ML model can perform the right processes with the data fed to it, there might be issues beyond training data and AI/ML algorithms that can restrict the model from functioning effectively. Consider this performance-related issue: if the model exhibits a lag while producing results, that might not help in certain use cases. Taking the example of an autonomous vehicle, if it takes even as long as a second to identify a pedestrian in the middle of a street, the vehicle might still end up causing an accident. Factors surrounding performance influence real-life consequences, so it’s important to identify such issues.

Mixing up correlation and causation

It’s easy to allow your ML model to function based on correlating certain data points consumed to determine a cause. Consider this conflation of correlation and causation: “The faster windmills rotate, the more wind is observed. Hence wind is caused by rotation.”

While that statement’s fault might seem obvious to us, it might be fair logic to an AI/ML model’s mind. In most cases, acting based on correlation may not have significant adverse consequences. But, it displays an inaccuracy in the model’s algorithm. Ideally, correlation and causation shouldn’t be misunderstood, even by an AI/ML model.

Conclusion

Training an AI model is no simple feat. It involves a comprehensive understanding of the human mind and a serious attempt to replicate it. We’re making great strides in the science of Artificial Intelligence, but we still have ways to go. We can traverse those ways faster if we identify and eliminate key mistakes that make our model’s performance suffer. And we can do that only if we understand the common mistakes that we need to avoid while training and developing our AI models.