What Are the Top Mathematical Technologies That Traders Use Today

Algorithmic formulas are allowing quant trading to take over the financial capitals of the world. Math technologies are helping even the most novice traders conquer the stock market. But with so many trading software in the market, how do you choose the best one to use? The programming language on which the software is built is an excellent place to start.

Which Programming Language Is Best for Trading Software

The best programming language for trading is, by and large, determined by the transparency and ready-made features that the software built on it makes possible. Other things to consider are its strategy parameters, resiliency, general performance, and cost.

Excellent algorithmic trading software should include second-to-none research tools, execution engine, risk manager, and portfolio optimizer. Faulty software or one lacking the necessary features could be the reason you incur huge losses of your hard-earned cash. The following are some of the most preferred programming languages for trading software.

R

R programming language has been a choice language for statisticians and academics for over two decades. It is often the go-to programming language for statistical analysis. Primarily, R does what spreadsheets do, but faster and with greater ease.

What makes it stand out as a trading software is its benefits in data wrangling (tidying up data for use), data transformation (creating custom data sets), data analysis (executing statistical models), and practically all machine learning and visualization forms. R makes algorithmic trading a somewhat straightforward undertaking. That said, some intrinsic limitations show up as a trader’s needs increase.

Python

Python easily stands out as a trading programming language because of the ease with which it can be deployed in automated trading systems and machine learning. It is quite easy and straightforward for beginners to learn. It also has exclusive library functions that make it easy to code strategies in algorithmic trading.

Many traders prefer Python over C because it is faster in evaluating mathematical models. Given the centrality of speed in high-frequency trading, the less trading strategy time that Python affords the trader is a big part of its allure. However, it is somewhat slower than C++.

MATLAB

Like R, MATLAB is a programming language of choice for quantitative traders and researchers. Because of its focus on technical computing, MATLAB is an excellent choice for automated trading. What’s more, MATLAB is an integrated development platform with a user-friendly interface and debugger.

MATLAB easily stands out in backtesting compared to Visual Basic or Excel because it has an extensive database of built-in functions that are extremely helpful mathematical computations. For traders analyzing a large number of stocks simultaneously, MATLAB’s matrix manipulation and processing make such calculations as easy as analyzing a single stock. That said, it can be restrictive and somewhat risky when it comes to availability.

MQL5

MetaQuotes Language 5 (MQL5) is designed for algorithmic trading and is supported by a powerful community of helpful, highly skilled developers. It is an excellent programming language for creating utility applications, trading robots, and technical indicators that automate financial trading. Unlike other programming languages, MQL5 is designed primarily for financial trading. It, therefore, comes with an impressive list of built-in technical analysis functions and trade management features.

On top of its ease of use and extensive features, MQL5 is also fully compatible with R and Python. What that means is you can leverage the power of the most advanced programming languages within the MQL5 development environment. With that, you have the best of both worlds.

MQL5 makes it relatively easy to create automated financial trading and market analysis applications. Through the use of MLQ5 and other languages like R and Python, you can perform practically any type of data analysis and trade operation you can think of. On top of that, it makes it easy for traders to carry out trading operations and technical analysis in stock and forex exchange markets.

How to Choose the Best Trading Software

The best automated algorithmic trading software makes it easy to trade and increases profitability. Instead of creating custom trading software and platforms, the better approach is finding a trading application that checks all the necessary boxes to turn a profit. Here are some of the things to check for in trading software:

Supports all markets

The software you choose for financial trading should accept feeds in different formats, including FIX, Multicast, and TCP/IP. Go for algorithmic trading applications with the ability to process aggregated market feeds from an array of exchanges.

The best trading software should allow you to trade in different markets over multiple accounts while leveraging several strategies simultaneously. For instance, MetaTrader 5 enables hedge funds to diversify their trades and, as a result, spread their risk over many instruments and markets.

Offers data analysis tools

Traders have to keep tabs on the goings-on in the market in real-time. Without up-to-date information, the decisions you make as a trader could result in losses that could have been avoided. Go for trading software with data analysis tools that give you insights into what’s happening on trading floors live. Well-designed trading software like MetaTrader 5 goes a notch further; they show you visual trading representations through bars, broken lines, and Japanese candles.

Full transparency

Any trading software whose market and company data is not readily available for you to review is a no-go-zone. Algorithmic trading uses your hard-earned cash. You must ensure you know enough about the software you’re about to use for trading as well as the company that built it.

Go for trading software that values transparency. Work with a company that explains how they invest their money and the profit your investment is making—the ability to see the company data should be a built-in feature in the software.

Automated and fully prepared robots

The whole idea of leveraging mathematical technologies in trading is to make an otherwise tricky trading process possible and easy even for novice traders. Therefore, working with a trading platform with automated robots that do all the heavy lifting makes sense. For instance, MetaTrader 5 trading robots can analyze financial instruments quotes and execute trade operations on exchange and Forex markets.

The software also allows hedge funds to create, test, debug, implement, and optimize trading robots. And if the robots available are falling short of your requirements, there’s an option to order a trading robot to be custom-built for you.

One-click communications

Few things are as dreadful as investing money in an opaque trading platform where it’s impossible to tell where your money is and whether it’s returning a profit. To avoid the worry and uncertainty that such scenarios can bring, use trading software with one-click communications.

Trade with applications that make it easy to ask questions and get answers back. MetaTrader 5 goes a step further: it eliminates uncertainty by offering real-time fund performance with detailed reports to help you keep tabs on your algorithmic trading round the clock.

Final Thoughts

Building your own trading software is complex and often overwhelming, and most algorithmic trading software is costly. Yet, there are excellent options that live up to the pressures of exchange and forex trading and increase the chance that you’ll turn a profit. Few algorithmic trading software can match the power of MetaTrader 5 for hedge funds.

Users of this trading software can harness its capabilities by integrating it into practically any brokerage account. And with expert robotic advisors that implement automated strategies, the trading floor is open even for people with little or no programming and trading experience. In this highly competitive world, choosing the right mathematical technologies for trading is often the difference between making lots of money and wiping out your investment.

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Central Limit Theorem for Non-Independent Random Variables

The original version of the central limit theorem (CLT) assumes n independently and identically distributed (i.i.d.) random variables X1, …, Xn, with finite variance. Let Sn = X1 + … + Xn. Then the CLT states that

that is, it follows a normal distribution with zero mean and unit variance, as n tends to infinity. Here μ  is the expectation of X1.

Various generalizations have been discovered, including for weakly correlated random variables. Note that the absence of correlation is not enough for the CLT to apply (see counterexamples here). Likewise, even in the presence of correlations, the CLT can still be valid under certain conditions.  If auto-correlations are decaying fast enough, some results are available, see here.  The theory is somewhat complicated. Here our goal is to show a simple example to help you understand the mechanics of the CLT in that context. The example involves observations X1, …, Xn that behave like a simple type of time series: AR(1), also known as autoregressive time series of order one, a well studied process (see section 3.2 in this article).

1. Example

The example in question consists of observations governed by the following time series model: Xk+1 = ρXk + Yk+1, with X1 = Y1, and Y1, …, Yn are i.i.d. with zero mean and unit variance. We assume that |ρ|  <  1. It is easy to establish the following:

Here “~” stands for “asymptotically equal to” as n tends to infinity. Note that the lag-k autocorrelation in the time series of observations X1, …, Xn is asymptotically equal to ρ^k (ρ at power k), so autocorrelations are decaying exponentially fast. Finally, the adjusted CLT (the last formula above) now includes a factor 1 – ρ. If course if ρ = 0, it corresponds to the classic CLT when expected values are zero.

1.2. More examples

Let X1 be uniform on [0, 1] and Xk+1 = FRAC(bXk) where b is an integer strictly larger than one, and FRAC is the fractional part function. Then it is known that Xk also has a uniform distribution on [0, 1], but the Xk‘s are autocorrelated with exponentially decaying lag-k autocorrelations equal to 1 / b^k. So I expect that the CLT would apply to this case. 

Now let  X1 be uniform on [0, 1] and Xk+1 = FRAC(b+Xk) where b is a positive irrational number. Again, Xk is uniform on [0, 1]. However this time we have strong, long-range autocorrelations, see here. I will publish results about this case (as to whether or not CLT still applies) in a future article.

2. Results based on simulations

The simulation consisted of generating 100,000 time series X1, …, Xn as in section 1.1, with ρ = 1/2, each one with n = 10,000 observations, computing Sn for each of them, and standardizing Sn to see if it follows a N(0, 1) distribution. The empirical density follows a normal law with zero mean and unit variance very closely, as shown in the figure below. We used uniform variables with zero mean and unity variance to generate the deviates Yk.

Below is one instance (realization) of these simulated time series, featuring the first n = 150 observations. The Y-axis represents Xk, the X-axis represents k. 

It behaves quite differently from a white noise due to the auto-correlations.

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About the author:  Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). He recently opened Paris Restaurant, in Anacortes. You can access Vincent’s articles and books, here.

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Why saying “We accept the Null Hypothesis” is wrong. – An Intuitive Explanation

We often come across YouTube videos, posts, blogs, and private courses wherein they say “We accept the Null Hypothesis” instead of saying “We fail to reject the Null hypothesis”.

If you correct them, they would say what s the big difference? “The opposite of ‘Rejecting the Null’ is ‘Accepting’ isn’t it ?”.

Well, it is not so simple as it is construed. We need to rise above antonyms and understand one crucial concept. That crucial concept is ‘opperian falsification.

This concept or philosophy also holds key to why we use the language “Fail to reject the Null”.

Basically, the Popperian falsification implies that ‘Science is never settled’. It keeps changing or evolving. Theories held sacrosanct today could be refuted tomorrow.

The Popperian falsification implies that ‘Science is never settled’. It keeps changing or evolving. Theories held sacrosanct today could be refuted tomorrow.

So under this principle, scientists never proclaim “X theory is true”. Instead what they try to prove that “the theory X is wrong”. This is called the principle of falsification.

Now having tried your best and you still could not prove the theory X is wrong, what would you say? You would say “I failed to prove theory X is wrong”. Ah.. now can you see the parallels between “I failed to prove theory X is wrong” and “We fail to reject the Null ”.

Now let’s come to why you can’t say “we accept the Null hypothesis”.

We could not prove theory X is wrong. But does that really mean theory X is correct? No, somebody smarter in the future could prove theory x is wrong. There always exists that possibility. Remember above that we said, “science is never settled”.

A more classic example is that of the ‘Black Swan’. “Suppose a theory proposes that all swans are white. The obvious way to prove the theory is to check that every swan really is white — but there’s a problem. No matter how many white swans you find, you can never be sure there isn’t a black swan lurking somewhere. So, you can never prove the theory is true. In contrast, finding one solitary black swan guarantees that the theory is false.”

Note: The post is merely to drive home the point of how the language “we fail to reject” came about. It is not a post favoring inductive reasoning over deductive reasoning or vice versa. Neither it is an effort to prove or disprove Karl Popper’s falsification principle.

Reference (Black swan example): https://www.newscientist.com/people/karl-popper/#ixzz70d4aPeIj

Your comments are welcome. You can reach out to me on

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PLM as a backbone for Disruptive Digital Thread

Product complexity is on the rise. Manufacturers need to understand customer requirements, define, and design products, collaborate with global designers and suppliers, ensure material feed and recipe integration, use varied manufacturing techniques, seek regulatory approvals, make processes and products sustainable, keep pace with shorter product lifecycles…there are too many moving parts in manufacturing making it extremely challenging to meet quality, cost and time-to-market goals while staying competitive. What modern manufacturing needs is a giant can of WD-40 to make all the parts interact and work smoothly. That can is Product Lifecyle Management (PLM).

PLM is designed so that the domains of engineering, manufacturing, and distribution do not have to work in siloes. Industry 4.0 technologies such as IoT, AR, VR, and 3D Printing have become a catalyst to reduce the gap between these domains even further. Modern PLM integrates Industry 4.0 technologies, effectively stitching together once-isolated clusters of knowledge: PLM becomes the digital thread running across domains, like a system-of-systems, binding the value chain of development, manufacturing, and distribution.

Some of the world’s leading manufacturers know how difficult it can be to propagate changes made to one system or product configuration across domains. Every department, from supplies to manufacturing, marketing to sales, and distribution to service, needs up-to-the-minute information on product changes so that it can continue to meet its KPIs efficiently.

Achieving efficiency in a digital environment lies in channeling feedback from design, manufacturing, sales, service, recycling, etc., to rapidly evolve products and portfolios. As the clusters of knowledge grow, from design to end of life, PLM should allow the complex changes to flow flawlessly across the lifecycle of the product. Industry forecasts show that the demand for these capabilities, coupled with rapid digital adoption, will see the market for PLM grow from US$50.7 billion in 2019 to US$73.7 billion by 2024.[i]

While PLM has done well in discrete manufacturing, it is about to make a huge dent in process industries such as oil and gas, paper products, textiles, and chemicals. PLM is no longer constrained by on-premise infrastructure—which has traditionally taken long to get off the ground. Today, PLM has become available in the cloud, with attractive cost and time-to-implement models.

PLM will become central in process manufacturing to optimize operations and take rapid decisions. Manufacturers adopting PLM will also be able to examine the previously isolated clusters of wisdom—say, design, sourcing, and costing—with a right click of the mouse. They will be able to tell when a component in a plant is about to fail, the impact of the failure on downstream processes, and how they can avoid shutdowns. They will be able to navigate changes, from design and build to operations and recycle, in an instant. And they will be able to automate their decisions, change recipe cards and plant arrangements to meet dynamic demand changes with the least possible downtime. Manufacturers hoping to maximize ROI from their digital investments and industry 4.0 technologies will make PLM their ticket to success.

[i] https://www.marketsandmarkets.com/Market-Reports/product-lifecycle-…

Authors:

Adnan Ghauri

Director Enterprise Architect-PLM

Baker Hughes

Akhil Jain

Vice President-PLM

ITC Infotech

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DSC Weekly Digest 13 July 2021

I talk to a lot of people involved in the data science and machine learning space every week – some vendors, some company CDOs, many just people in the trenches, trying to build good data models and make them monetizable.

When I ask what part of the data science pipeline they have the hardest part with, the answer is almost invariably “We can’t get enough good data.”

This is not just a problem with machine learning, however. Knowledge Graph projects have run aground because they discover that too much of the data that they have lacks sufficient complexity (read, connectivity) to make modeling worthwhile. The data is often poorly curated, poorly organized, and lacking in semantic metadata. Some data, especially personal data, is heavily duplicated, has keys that have been lost in context, and in many cases cannot in fact be collected without a court order. Large relational databases have been moved into data lakes or enterprise data warehouses, but the data within them often heavily reflects operational rather than contextual information, made worse by the fact that many programmers have at best only limited training in true data modeling practices.

What this means is that the content that drives the initial training of the data model is noisy, with the signal so weak that any optimizations made in the model itself may put the data scientist into a position where they are able to reach the wrong conclusions faster.

Effective data strategy involves assessing the acquisition of the data from the beginning, and recognizing that this acquisition will require the expenditure of money, time, and personnel. There are reasons why data aggregators usually tend to benefit heavily from being early adopters – they discovered this truth the hard way, and made the investment to make their businesses data scoops, with effective data acquisition and ingestion strategies rather than just assuming that the relational databases in the back office actually had worthwhile grist for the mill.

As data science and machine learning pipelines become more pervasive in organizations and become more automated, through MLOps and similar processes, this need for good source data is likely to be one that every organization’s CDO needs to attend to as soon as possible. After all, garbage in can only mean garbage out.

In media res,

Kurt Cagle
Community Editor,
Data Science Central

To subscribe to the DSC Newsletter, go to Data Science Central and become a member today. It’s free! 

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13 Chatbot trends and statistics you can’t afford to miss in 2021!

Machine learning is the technology behind the development of chatbots which involves:

• A computer program that learns and evolves
• Natural language processing (NLP) to mimic human-generated text and language

Before the advent of the internet, face-to-face meetings and phone calls dominated the communication landscape. Years later, online forms, mobile apps, social media, and emails have taken over as modern forms of communication.

A multitude of industries employ chatbots that help visitors navigate through a website or answer their questions. Given the highly competitive market, customers have increased expectations from brands.

Incorporating chatbots help meet those expectations. Also, AI is constantly evolving, which means that chatbots will become more sophisticated in the future. Hence, we will discuss AI chatbot trends in 2021 that you should consider adopting in your strategy. 

Let’s brush up on your knowledge of chatbot statistics 2021.

Key Chatbot Statistics

Before going into the chatbot statistics for 2021 and beyond, it’s essential to look at the state of the conversational AI market overall.

Conversational AI Market 

Research suggests that the spending on will surpass $77 Bn by 2022. The forecast for 2019 was $35.8 Bn, which means that it will likely double by next year. A third of that spending will be incurred on software.

Besides, a significant chunk of software spending will go towards AI applications such as chatbots and personal assistants (nearly $14.1 billion). 

Apart from that, the market is anticipated to experience a stellar growth at 30.5% CAGR (compound annual growth rate) between 2017-2022. In terms of market size, North America leads the global conversational AI market.  

However, when it comes to CAGR, Asia Pacific (APAC) will experience the fastest growth during the forecast period. Besides, the adoption of conversational AI software is widespread in North America.

An increasing government spending, large number of chatbot companies, and rising investments in artificial intelligence and machine learning are responsible for the adoption.

Chatbot Forecasts & Predictions

Listed below are key chatbot forecasts and predictions, divided by year:

2021

• About 50% of businesses will increase their annual expenditure on Chabot creation compared to conventional mobile app development.
• Chatbots are expected to see a whopping investment of $4.5 billion by 2021.
• The implementation of artificial intelligence in customer service interactions is anticipated to rise by 400% between 2017-2021.
• AI will manage approximately 85% of customer interactions by 2021. 

2022

• Businesses across different domains would save nearly $0.70 per customer interaction, thanks to chatbots (CNBC). 
• About 90% of customer interactions with banks would be automated using chatbots by 2022 (Juniper Research). 
• Chatbots are projected to save almost $8 billion annually for businesses by 2022. 
• About 70% of white-collar employees would regularly interact with chatbots.

2023

• Businesses and customers will save 5 billion hours on interactions because of chatbots by 2023. 
• Chatbot eCommerce transactions are projected to surpass a value of $112 billion by 2023.
• The banking, retail, and healthcare sectors will save nearly $11 billion a year by employing chatbots in customer service.
• The global chatbot market is expected to observe a double-digit growth at 34.64% CAGR between 2017-2023. 

2024

• The global conversational AI market is forecasted to reach $15.7 billion at 30.2% CAGR by 2024. 
• AI will redefine 50% of user experiences using natural language, computer vision, augmented reality, and virtual reality. 
• The global chatbot market is predicted to observe an impressive growth at 34.75% CAGR between 2019-2024. Moreover, it will cross $7.5 billion by the end of 2024.

2025

• Nearly 95% of customer interactions (online conversations and live telephone) will be taken over by artificial intelligence by 2025. However, at this point, it will be pretty tricky for customers to differentiate a chatbot from a human agent. 
• Businesses that leverage AI to automate their customer engagement will observe a 25% increase in their operational efficiency. 
• The annual revenue of the global AI software market will cross $118 billion by 2025. 

2026

• By 2026, the global chatbot market will touch $10.8 billion with an impressive CAGR of 30.9% during the 2018-2026 forecast period. 

These statistics indicate that chatbots are the future. However, with further advancements in AI, only capable and intelligent conversational AI platforms shall persist. 

In the next section, we will look at the chatbot trends more closely.

Chatbot Trends in 2021

Given the many advantages of chatbots, rising chatbot trends, and the ever-increasing popularity of machine learning (ML) and artificial intelligence, adopting new technologies has become business-critical.

Here are the top 13 chatbot trends in 2021 that you should consider embracing:

1. Payments via Chatbots

First on our list of chatbot trends is payment gateways. Several payment services, including Paypal, have incorporated chatbots in their payment gateways and digital wallets in 2020.

This shows that the application of chatbots is no longer limited to just customer service. For example, users simply have to type “pay electricity bill” in this application.

The  would walk them through the process until the final payment is made. 

2. Voice Bots

Voice-driven search, thanks to the advent of conversational AI, is taking the world by storm. It is the next big thing. According to a report by Accenture, most consumers prefer voice-based interfaces over text-based interfaces on messaging platforms.

The trend of utilizing conversational bots to assist consumers over both voice and text is on the rise. In sectors such as education, travel, and insurance, voice bots would be of great help. 

It’s all about providing a seamless user experience!

3. Chatbots with Human Touch

Chatbots use artificial intelligence to suggest options when a customer types in their inquiry. Year after year, they are becoming increasingly sophisticated in terms of mimicking human conversation. 

They will identify the right intent behind a query and provide an accurate response to that query. By learning from interactions, chatbots are beginning to pick up patterns in user behavior. 

Hence, chatbots are becoming indispensable for high-impact conversations.

4. Chatbots with Emotional Intelligence

This is slowly becoming a reality with newer, groundbreaking technology. For instance, facial feature detection AI software can detect the feelings of a person.

Similarly, chatbots with emotional intelligence can figure out your mood (happy, sad, or angry) by looking for patterns in your text. It could be capitalization or punctuation or your voice to predict your emotions.

Conversational AI with soft skills will further humanize the interaction between businesses and their customers. 

5. Chatbots based on Natural Language Processing

Chatbots driven by NLP are groundbreaking for businesses that have more excellent customer service scenarios. They can determine the user intent and generate responses accordingly.

They learn from past conversations with the customer to provide accurate answers. The Royal Bank of Scotland (RBS), for instance, has already incorporated NLP-powered chatbots to enhance their customer experience.

Check out this blog to learn more about how custom chatbots can help you improve customer satisfaction.

In 2021, you will observe more and more organizations adopting  to improve conversations. 

6. Analytics and Insights with Chatbot

To measure your performance, you need to track and evaluate data. And for that, you need chatbot analytics and insights. The good news is most chatbots today offer analytics so that you can continue to improve your strategies.

They keep a record of:

• every question asked
• every answer delivered
• every query transferred to agents

Use this information to improve your product or service. Therefore, chatbots allow you to foster strong customer relationships. 

7. Multilingual Capabilities

Next on our list of chatbot trends is multilingual capabilities. Did you know that only 20% of the world’s population speaks English? So if you plan to expand your business globally or already cater to a global customer base, you need chatbots with multilingual capabilities. 

This will not only offer a boost to your localization efforts but also amplify your reach. But, again, it’s because customers prefer to use their native tongue to communicate with their trusted brands. 

Multilingual bots enable you to tap into new markets and personalize the experience for your audience.

8. Supporting Remote Workforce

A recent report by Gartner says that about 75% of business leaders would allow 5% of their employees to work from home post-pandemic permanently.

Apart from that, 25% agreed to permanently shift 10% of their employees to the remote work setting. Hence, it’s safe to say that such a work culture is here to stay.

In such a scenario, chatbots would become pivotal in answering typical employees’ concerns. 

They can:

• answer FAQs on remote work policies
• track employee health
• notify employees of the latest changes in policies
• offer work from home tips

9. Chatbots on Social Media

Social media is the hub of much social interaction in present times. We have moved on from just making friends on social media to voicing opinions, ordering products and services, offering reviews, and even getting in touch with businesses. Thus, it becomes a necessity for businesses to use chatbots to facilitate interaction on these platforms.

Many industry leaders in various sectors have already employed chatbots to use this vital resource to better understand the customer needs and even improve ways that the business can help the consumers.

Facebook already has a chatbot feature, but it is very limited in its capabilities; perhaps it was only a test to whether chatbots would fare well on the platform. The answer is a resounding yes. Facebook has now become a trendsetter in equipping businesses with the ability to use customized chatbots made by other parties to help in this process. Every social media platform is likely to follow suit.

10. Chatbots Built with Low Code Platforms

The pandemic forced companies to become more agile in their processes. This, in turn, led to the development of low-code chatbot platforms that allow businesses to deploy apps at a much faster rate.

Even less experienced users can build chatbots using these low-code development platforms.

Many organizations have deployed chatbots for sales support, customer support, service desk management, and more with this approach.

11. Recruiting and HR Chatbots

A recruiting chatbot can filter candidates, answer their basic questions, and schedule them for interviews. As a result, they speed up the entire recruitment process.

These chatbots are helpful even after the hiring process is over. During onboarding, they can answer the most basic HR questions. This is undoubtedly a boon for large organizations with massive workforces.

12. Self-Learning Chatbots

To stay ahead in the race, it is of utmost importance to train bots with new data and keep them up-to-date. In 2021, you can expect companies to make chatbots that are self-learning.

This means companies do not have to spend time feeding new data to bots. They will analyze the pattern in every interaction and train themselves to keep the users or customers engaged. In other words, bots will learn to improve their response capabilities based on user feedback.

13. Text Messaging with Chatbot

While every other mode of communication, like email and phone calls, still holds ground, the most personal still remains – a personal message. SMS and WhatsApp are the go-to apps that people regularly check and are comfortable conversing over.

In 2021 and beyond, chatbots are going to be leaning onto this opportunity to better connect with audiences. Many companies like Yatra and MakeMyTrip already use certain chatbot features to send flight tickets directly to Whatsapp and the details via SMS. This has made the process convenient for users, and any progress would only make things easier.

In 2021, you will see SMS and create a personalized experience and facilitate open-ended conversations.

The Future of Chatbots

In a rapidly maturing conversational AI market, haphazardly placed chatbots will vanish. Only the ones that are strategically developed and implemented across channels shall survive.

Across all digital platforms, millions of users will prefer voice-enabled conversational AI to interact with an enterprise. Such is the future of chatbots. Moreover, various chatbot applications will no longer remain siloed.

Businesses would be able to create an intranet of chatbots that can share information and work together seamlessly. In the meantime, chatbots will continue to boost user engagement and enhance customer support. 

This article was originally published here

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The risk of using ‘half-baked’ data to address the challenges posed by COVID-19

As COVID-19 rampages across the globe it is altering everything in its wake. For example, we are spending on essentials and not on discretionary categories; we are saving more and splurging less; work-life balance has a deeper focus on mental health; we are staying home more and traveling less. Our priorities have changed. If you look at this unfolding scenario wearing a data hat, the facts and knowledge we relied upon to forecast markets have been rendered practically useless.

We saw evidence of this in late March when the pandemic took root in western nations. There was a surge in demand for toilet paper, fueled by panic-buying, leading to an 845% increase in sales over last year.[i] The most powerful analytic engines belonging to the world’s largest retailers could not forecast the demand. Reason: models used by analytical engines are trained on existing data and there are no data points available to say, “Here is COVID-19; expect a manic demand for toilet paper.” Businesses know that their investments in digital technology turned out to be the silver lining in the new normal, but they also learnt that depending on the current stockpile of data can lead to blind spots, skewed decisions and lost opportunities.

While the pandemic will leave a profound impact on how the future shapes up, it is providing data scientists with plenty to think about. They know that the traditional attributes of data need to be augmented to deliver dependable and usable insights, to deliver personalization and to forecast the future with confidence.

When the underlying data changes, the models must change. For example, in the wake of a crisis, consumers would normally choose more credit lines to tide over the emergency. But they aren’t doing that. This is because they know that their jobs are at risk. They are instead reducing spends and dipping into their savings. Here is another example—supply chain data is no longer valid, and planners know the pitfalls of using existing data. “It is a dangerous time to depend on (existing) models,” cautions Shalain Gopal, Data Science Manager at ABSA Group, the South Africa-based financial services organization. She believes that organizations should not be too hasty to act on information (data) that could be “half-baked”.

There is good reason to be wary of the data organizations are using. Models are trained on normal human behavior. Given the new developments, it must be trained on data that reflects the “new” normal to deliver dependable outcomes. Gopal says that models are fragile, and they perform badly when they have to handle data that is different from what was used to train them. “It is a mistake to assume that once you set it up (the data and the model) you can walk away from it,” she says.

There are 5 key steps to accelerating Digital Transformation in the “new normal” which dictates how an organization sources and uses data. These provide a way to reimagine data and analytics that lays the foundation for an intelligent enterprise and helps derives maximum insights from data:

• Build a digitally enabled war room for real-time transparency, responsiveness and decision-making
• Overhaul forecasting to adapt to the rapidly changing environment with intelligent scenario-planning
• Rebuild customer trust with personalized digital experiences
• Invest in technology for remote working, operational continuity and security
• Accelerate intelligent automation using data

Events like the Great Depression, 9/11, Black Monday, the 2008 financial crisis, and now the COVID-19 pandemic, are opportunities to create learning models. Once the Machine Learning system ingests what the analytical models should see, forecasting erratic events becomes easier. This implies that organizations must build the ability to maintain and retrain the models and create the right test data with regularity.

ITC Infotech recommends 6 steps to reimagine the data and analytics approach of an organization in the new normal:

1. Harmonize & Standardize the quality of data
2. Enable Unified data access across the enterprise
3. Recalibrate data models on a near real-time basis
4. Amplify data science
5. Take an AI-enabled platform approach
6. Adopt autonomous learning

The ability to make accurate predictions and take better decisions does not depend solely on connecting the data dots—it depends on the quality, accuracy and completeness of the data. Organizations that bring data to the forefront of their operations also know that it is important to understand the right dataset, what the data is being used to solve. In effect, data and analytics have many moving parts. These have become especially important in the light of the changes being forced by COVID-19. Now, there is a rare window of opportunity in which organizations can rapidly adjust their approach to data—and gain an advantage that conventional business wisdom cannot match.

[i] https://www.chron.com/business/article/Toilet-paper-demand-shot-up-…

Co-Authored by :

Shalain Gopal

Data Science Manager, ABSA Group

Kishan Venkat Narasiah

General Manager, DATA, ITC Infotech

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Detect natural disasters with the help of predictive analytics tools

Predictive analytics tools are a key asset in detecting natural disasters. With higher accuracy than other weather detection sensors, they can detect early signs of an oncoming calamity to prevent mistakes like the one that happened in 2016.

On 28th September 2016, weather sensors picked up a storm moving westward of the Lesser Antilles island chain. The storm was a category one threat, which was concerning but manageable. 

Within 24 hours, the threat level increased from category one to category five, turning a storm into a hurricane. 

The hurricane, known as Hurricane Matthew, would go on to inflict immense damage across the Caribbean and the southeastern United States, making its final landfall on October 8th in South Carolina, leaving untold damage in its wake. (The name “Matthew” would eventually be retired because of the damage incurred). 

The National Center for Environmental Information (NCEI) estimated that over $10.3 billion were lost in property damage. While the World Vision assessment team estimated that southwestern Haiti lost over 80-90% of its housing, the damage done to staple food crops would take (at the time) five years to recover from. The devastation inflicted by the hurricane was immense.

However, what is particularly worrying for local and national governments is the growing number of natural disasters.

Research shows that the number of natural disasters is growing—Statistica indicates that the number of natural disasters that took place in 2020 was 416, while there were 411 natural disasters in 2016 (the year Hurricane Matthew devastated Haiti and much of the Caribbean). 

More concerning than this number, is the frequency of natural disasters. Over 207 disasters were recorded from across the world, in the first six months of 2020 alone. 

These disasters are incredibly costly (global costs are estimated at $71 billion), and as Hurricane Matthew shows, can be difficult to predict. 

Amidst such a turbulent environment, what local and national governments need are tools that could help them better anticipate these devastating events. 

The ability to anticipate natural disasters will give them the ability to plan emergency responses and procedures that could mitigate the damage from these events. 

This is where predictive analytics tools play a crucial role. 

Predictive data tools can refine the so meteorologists can make more accurate predictions when a natural weather phenomenon turns into a disaster. 

The science behind predictive analytics tools and natural disasters

The secret is in big data. Previous natural disasters have generated plenty of information, like rainfall, wind levels, and weather patterns, that can be extracted.

Predictive analytics software tools can collect, clean, and analyse this data to gain useful insights into natural disasters. This allows weather departments to better detect the early warning signs in any weather phenomenon, so they will know if a category one rainfall will turn into a category five storm. 

Machine learning algorithms within the analytics platform can collect and analyse the data. The more data it is fed, the deeper the level of understanding the system builds, on the difference between natural disasters and normal weather. 

When a normal weather phenomenon occurs, data analytics platforms can study the weather patterns and compare them against data on previous natural disasters. If current weather patterns match previous data findings, it is a sign that a disaster is impending. 

This is invaluable because meteorologists can use predictive analytics tools to refine the process of detecting natural disasters. They can detect the early warning signs a lot sooner, allowing them to make accurate calls on time, preventing errors similar to what happened with Hurricane Matthews.

Analytics tools can improve weather detection in other areas, for example, refining early warning systems for better accuracy. 

Better natural disaster practices benefit other areas related to disaster management, like emergency response and disaster relief. Local and national governments can improve relief measures and response protocols because they will know what constitutes an emergency and what doesn’t.

Preparing for the future with analytics platforms

Research shows a disturbing trend where natural disasters are growing in frequency due to climate change. While local and national governments cannot undo the causes behind this development overnight, they can improve their response procedures and disaster relief measures to mitigate the damage from these disasters. 

Predictive analytics tools can help in this goal by improving detection methods as analytics platforms pull from previous data sources to improve the detection process and remove uncertainties that could compromise accuracy in natural disaster detection. 

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