How manufacturing data analytics is driving production automation

Manufacturing is arguably the most important industry in an economy, as it makes everything we use and love. It’s safe to say that the manufacturing industry has had a hand in making the world as we know it today and will continue to do so in the years + decades to come.

From the advent of coal and steam power to the adoption of electricity, the industry has reinvented itself several times over the last few hundred years. In recent years, thanks to advancements in engineering technology, production optimisation, and better quality control processes, the industry has grown leaps and bounds.

Now, as the world heads into the fourth industrial revolution, however, there is a growing need for the manufacturing industry to adopt novel and independent manufacturing technologies that can not only help handle contemporary challenges but also minimise the use of human resources in the production process.

There is consensus among manufacturers across the globe that production automation is the way to move forward in industry 4.0, and manufacturing data analytics is the key to implementing efficient automation processes that deliver better efficiency and productivity compared to human resources.

Data analytics is not a new concept for the manufacturing industry, but the dynamics of modern manufacturing demands greater integration of data analytics to deliver automated production processes that increase plant predictability, agility, and quality control.

In this post, we take a deeper look at how manufacturing data analytics drives modern automation efforts in the manufacturing industry.

How is data analytics used in the manufacturing industry?

Manufacturing data analytics platforms collect data and analyse it to reveal useful insights, which can be leveraged for various decision-making purposes. The additional insight makes these solutions incredibly useful across the production chain. 

From equipment maintenance to studying market trends, manufacturing analytics can be leveraged for different use cases to make decisions that generate a higher ROI and lower operational costs.

The use of edge analytics to build smarter production equipment

Unlike in traditional manufacturing, modern production hardware is always connected to the web using a wide range of IoT devices in the form of sensors and cameras. These IoT devices collect and transmit data to centralised data processing systems to produce insights that help optimise the next production cycle.

That said, the increasing pressure to produce quality throughput on manufacturing plants means that they can’t wait until the next production cycle to optimise processes and minimise errors.

With edge analytics, instead of streaming collected data to centralised data analytics systems, manufacturers can build smart manufacturing equipment that process data in-device, and make real-time optimisations in the production process—ensuring tighter tolerances and reduced resource wastage.

Material handling in manufacturing plants, for example, relied heavily on human resources until recently. Today, with the use of manufacturing data analytics, manufacturers have automated material handling by using Automated Material Handling Systems (AHMS) that detect material requirements and deliver them to the particular workstation—making the interaction between different stages of the production process seamless.

The integration of robotics in the manufacturing process

Traditionally, manufacturers relied on human intensity to conduct tasks that require extreme precision or a high level of complexity—think businesses operating within the fashion and automotive industries.

That said, now, due to advancements in manufacturing data analytics, artificial intelligence, machine learning, and robotic technology, manufacturers can leverage advanced robotics systems that handle complex tasks that require a high level of precision.

Adidas, for example, has opened a robotic manufacturing plant, dubbed the Speedfactory, based in Germany, which utilises robots in every step of the production process—reducing the six-week shipping time from Asia, where Adidas produces the majority of its 400 million shoes per year.

Another fashion giant, Zara, operates 14 robotic apparel factories in Spain, which deliver new fashion products to the showroom floor within ten days of finalising the design. 

When it comes to robotics in the production process, we can’t miss the automotive industry, as it was one of the early adopters of production automation through robotics. Almost all of the major auto brands utilise robotics in their production process to some extent. Tesla is even building multiple Gigafactories around the world that use fully automated production processes.

How can manufacturers benefit from data analytics?

When considering the big picture, manufacturers benefit from data analytics through a significant reduction in uncertainty across decision-making. 

Today, senior executives often make choices with incomplete sets of information that lead to unforeseen consequences. What’s more, visionary and innovative choices may be perceived as risky because there is a lack of evidence to back up the practicality of the ideas behind them. 

Manufacturing data analytics can eliminate the risk from the decision-making process by providing a complete set of information to back up more creative, intuitive choices.

Manufacturing data analytics is building the factory of the future

As the business world evolves and consumer preferences change, the manufacturing industry needs to reinvent itself one more time to keep up with the growing demand. 

Manufacturing automation and data analytics hold the key to building future factories that are smarter and more efficient.

What are the benefits of data analytics in manufacturing and how does it improve product quality?

In the truest sense of the word, manufacturing is the engine of the world economy. In fact, the manufacturing industry is one of the most valuable industries to any economy. 

For example, the GDP of the two largest manufacturing hubs in the world, China and the USA, had contributions of $4 trillion and $2.3 trillion respectively from the manufacturing industry.

Without manufacturing, we would not be able to access the products and services we love and use. From the cars we drive to the phones we communicate with, we depend on the manufacturing industry to deliver our beloved products to us.

Ever since the industrial revolution transformed how we approach manufacturing, the business world has been looking for new ways to streamline the process even more; leading companies are outsourcing their manufacturing processes to more efficient offshore plants, and manufacturers are automating their production processes to reduce human errors and improve quality.

With the help of these advancements, leading manufacturers across the globe can produce thousands of products with tighter tolerances and minimal human intervention.

The most critical advancement in the manufacturing industry in recent years, however, has been the integration of data analytics into the manufacturing process, which has given manufacturers enhanced production capabilities.

In this post, let’s explore the benefits of data analytics in manufacturing and how it’s improving product quality across all industries.

The benefits of data analytics in manufacturing

• Data analytics powers better product design

Traditionally, introducing a product design to the market involved a lot of trial and error. Most often than not, the first iterations of the product design had an underwhelming welcome among consumers due to the less than ideal ergonomics and design.

Today, however, with the power of data analytics, manufacturers can test their designs for efficiency and ergonomics without ever making a physical prototype. 

Modern data analytics tools utilise machine learning and artificial intelligence to create computerised product designs and help manufacturers put them through their paces.

In automobile manufacturing, for example, even wind tunnel testing has been moved to systems powered by data analytics.

• Automated manufacturing powered by analytics algorithms

We need to talk about the rise of automated manufacturing when considering the benefits of data analytics in manufacturing. 

Today, in many industries, the manufacturing process involves minimal human interaction—everything from delivering raw materials to quality control of the finished products is executed through advanced algorithms powered by data analytics.

The tight integration of data analytics also allows manufacturers to eliminate issues due to human error. A recent study found that 23% of unplanned idle time in the manufacturing process is due to human error. 

With automated manufacturing systems, manufacturers can minimise unplanned downtime by optimising the analytics algorithms to detect potential anomalies in the production process or equipment and conduct proactive maintenance.

• Efficient product management 

Deciding how much to produce is perhaps one of the most critical decisions across the entire production process. Overestimating the demand can lead to overproduction, costing the manufacturer millions of dollars in the process. 

Underestimating market demand, on the other hand, can tarnish the reputation of the company due to the untimely delivery of products and services. 

Therefore, manufacturers need to estimate their demand accurately to maximise their profit.

Data modelling and predictive analytics use historical demand data and simulate future market conditions to produce accurate demand forecasts for the future, helping manufacturers meet the market demand without wasting their resources.

The benefits of data analytics in manufacturing are powering the next industrial revolution

Today, many industries across the globe are going through the fourth industrial revolution—driven by the power of data analytics and digital infrastructure—and the manufacturing industry is no exception. 

The benefits of data analytics in manufacturing enable modern manufacturers to enhance capabilities to deliver more quality and efficiency in the production process.

How data analytics is improving marketing in the fashion industry

Fashion has always been one of the fastest-moving industries in the world. 

With its capricious trends and the tempestuous changes in consumer values, brands have to be on their toes to find what works, what doesn’t, and change their collections in line with that at break-neck speed. 

Especially with the disruption caused by COVID-19, most fashion houses and retailers found themselves on the back foot. With overnight halts across international supply chains and most consumers confined to their homes, the industry needed to take a step back and re-evaluate the direction it was headed in. 

In this environment, where trends come and go at dizzying speeds, brands need to be able to stay relevant without the trial and error. While data analytics have been a part of how fashion companies operate, their use is absolutely critical in the new normal of fashion marketing.

This is especially the case given the shift in consumer values and behaviour; it’s no longer enough for businesses to design clothes that are in style—brand values and messaging also need to hit the right notes. 

Here’s how data analytics is making all of that possible for the industry.

Knowing what to launch and how to position products

Following an unprecedented stint indoors, this year, many high fashion houses presented collections that addressed the demand for both casual glamour and overstated opulence people are craving after a year indoors.

This is just one example. 

As the seasons change and styles come and go (and come back again), predictive analytics can tell companies which products are likely to be a hit, and how and when they should be released. 

Especially when it comes to marketing campaigns, promotions and other outreach programmes, fashion companies need to get their timing right. 

By looking at the patterns and insights data analytics present us with, it’s easier to understand how consumers respond to certain products or collections. At a time when there’s both a resource scarcity and a greater onus to embrace more sustainable and ethical practices, the right data can help companies operate more resourcefully and create pieces that resonate with only the most relevant needs.

What’s more, these insights can also inform marketers about how to market the launches of new items and even promote and sell older silhouettes. 

Understanding how to frame brand messaging and marketing campaigns

Another very useful data analytics tool fashion companies should not miss out on is sentiment analysis.

Also known as emotion AI, these insights relate to the affective mood and other forms of subjective information that gives brands insights into how people feel. 

When applied in the context of fashion, brands can get almost immediate feedback via social media about how their collections or pieces resonate with their audiences.

These insights, in turn, can add significant value to the marketing messaging and approach companies take to market their products. 

By understanding how consumers are speaking about products and the emotions they associate with each brand, it’s easier to create messaging that resonates with what people are talking about, thinking, and feeling.

Especially in today’s context where marketing campaigns can’t be overly sales-oriented, must be conscientious, and align with community values, fashion companies risk reputational losses if they’re not in touch with how people feel about what they do.

Creating updated consumer profiles that reflect modern interests

The pandemic and its resulting effect on consumer behaviour and preferences have changed everything businesses thought they knew about the audiences they interacted with. For the fashion industry, this is truer than ever.

One way data analytics is helping companies navigate this uncertainty is by creating up-to-date customer profiles using data collected from various touchpoints. 

From social media engagements, previous interactions, and even website visits, fashion brands can take a look at how their consumers are behaving, what their interests are, products they’re interested in and other information, including personal information like age and gender identity.

These insights can then be compiled to create more insightful consumer profiles that inform marketing strategies and brand directions fashion houses and other mass manufacturers go down.

Today, these interests are likely to include sustainability, social justice, ethical sourcing and other best practices.

Fashion marketing has begun to change—and will only continue to do so—in the new normal

In a world where individualism and the expression of our identities are more celebrated than ever, fashion plays a profound role. 

With consumer habits, preferences, and even identities changing rapidly and in response to the mass upheavals we’re seeing in society, staying ahead of these shifts in demand and values is pivotal to the success of fashion companies.

At a time when companies like Shein and other fast fashion brands are being called out for their practices and values, understanding your consumers and connecting with them is the key to marketing success in the new normal.

What role does advanced analytics play in the casino industry?

Gambling is one of the oldest pastimes in human history, with first accounts of the activity dating back to 500 BC. It is also very popular—statistics show that at least 26% of the world’s population is involved in some form of gambling each year.

The casino industry, which includes all gambling activities, is also one of the fastest-growing industries. Even the pandemic didn’t slow down the growth—the casino industry grew at a faster rate during the last 18 months. In fact, according to Statista, the gambling market reached an all-time high of $227 billion in 2020.

The success and the spectacular growth of the industry are both because casinos always look for new and innovative ways to attract more customers, and keep the existing players hooked to the casino floor.

The industry was quick to identify the potential of internet-based casinos and splurged billions of dollars into creating and marketing digital casinos to hook a new wave of players on board. Now with a wealth of data available from on-site and digital casinos, the house can further optimise its games, security, and marketing campaigns with the power of advanced analytics.

In this post, let’s look at how the casino industry is using advanced analytics to revolutionise how they operate.

Better anti-cheat systems

Casino fanatics say that the house always wins, but this hasn’t stopped passionate and desperate players from using all means possible to win against the house, and some of these methods—like hand mucking, past posting, and card counting—aren’t legal.

Cheating methods like these have always been a bugbear of the industry; casinos invest millions of dollars into identifying and preventing cheating in their games, but these efforts have not been fail-proof.

With advanced analytics, however, casinos can analyse thousands of players playing online and on-site, and spot anomalies in their playing patterns, winnings, and general behaviour. This can help them implement more robust anti-cheat mechanisms in their games. 

Casinos can also educate their on-site floor attendants on using insights produced by analytics algorithms to detect cheating methods.

Optimising their games

To keep raking in dollars, casinos need to attract new players to their games, and the best way to do that is by optimising the odds in favour of the gamblers. While optimising their games, however, casinos should be careful not to stack the odds too much in favour of the gamblers. Otherwise, they will lose money on every game.

While game optimisation can be done by studying player behaviour and adjusting game mechanics, it takes a considerable amount of time and human resources to do that. 

That’s where analytics software can help.

Advanced analytics tools can collect and analyse vast amounts of data in a fraction of the time and produce actionable insights that help playmakers balance the odds to lure more players without sacrificing profit.

Streamline marketing campaigns

Marketing, for any business, is one of the most critical tools towards success, and the casino industry is no exception. An insightful and attractive marketing campaign reaches its intended audience and gives them information about the latest incentives or the newest games available to them.

Data analytics can help casinos identify their target audience for their online or on-site games and help create effective marketing campaigns by identifying customer preferences and needs, which marketers can then use to tailor the marketing message.

Moreover, online casinos can use data analytics to gather information about user experience, interface intuitiveness, and click-through rates and redesign their website and UI elements to make them more appealing to their target audience.

Advanced analytics is the game changer casinos were looking for

Gambling is a game of numbers. Both casinos and gamblers stand to lose everything or win a fortune simply by considering their odds carefully. 

With the face of the casino industry changing by the day, casinos need to look for more innovative ways to turn the odds in their favour, streamline their operations, attract more players, and stay profitable.

Data analytics can help them do that!

The role of predictive analytics in the insurance industry

Apart from the banking industry, insurance is perhaps the most critical sector in an economy. It provides businesses and individuals protection against financial risks and helps the government finance development projects without borrowing high-interest loans from local and foreign banks. 

It is no surprise then, that governments encourage citizens to invest in insurance policies, to help secure themselves against future medical emergencies, business losses, and retirement.

That said, like all other sectors in the global economy, the insurance industry was also impacted by the devastating consequences of the COVID-19 pandemic. 

The insurance market is experiencing unprecedented levels of asset risks, volatility in capital markets, and a lack of growth in the market.

In combination with the declining returns on equity in mature markets, these challenges have pushed insurance providers into investing in digital tools, like predictive data analytics, to improve operational efficiency.

In this post, we explore how predictive analytics tools are helping insurance companies improve their operations.

Why is predictive modelling important for insurance?

Insurance predictive analytics is an invaluable tool because it allows firms to operate smarter. Data analytics in the insurance industry helps agents anticipate future trends by analysing data, and these insights help them become more precise and accurate in their service offerings. For example, by assessing transactions in real-time, insurers can determine if certain requests or operations are safe or fraudulent. 

In essence, insurance firms can transition from firms that expend plenty of resources to remediate damage to firms that prevent damage.

What are the different types of predictive models?

Before we dive into how predictive analytics helps insurance companies optimise their processes, it’s important to understand what different options are available for them. These include:

• Forecast models
• Classification models
• Outlier models
• Time series models
• Clustering models

Now that we are aware of the different models available, we can dive into how they help insurance companies.

How do insurance companies use predictive analytics?

Predictive analytics in the insurance industry is applicable across a range of different functions and operations, all of which help companies work more efficiently than before. Here are a few applications of data analytics for insurance.

Pricing and risk assessment

No two insurance policyholders are the same. Depending on their level of risk, they will be paying different premiums even if they are subscribed to the same policy. That’s why risk assessment is perhaps the most important step in the insurance policy application process.

Insurance providers have always used data analytics in this process, but the recent changes in the industry have compounded the need for more robust data analytics tools like predictive analytics. 

Unlike traditional data analytics tools, predictive analytics utilises advanced machine learning and artificial intelligence algorithms. These collect, process, and analyse different types of data such as social media behaviour, criminal records, and credit reports to create a comprehensive and accurate risk assessment for each client.

Based on the predictive insights from these assessments reports, insurance providers can create a personalised pricing plan for individual customers.

Streamlining the claims process

In a traditional claims process, investigating each insurance claim takes weeks or even months, as investigation units use skills and experience to conduct their due diligence process. 

Predictive analytics, however, helps insurers streamline this process by giving insurers the ability to anticipate events before they occur and prioritise claims—shortening the claims investigation time and increasing customer satisfaction in the process.

Predictive analytics tools can also reduce the costs associated with the claims process. 

Researchers found out that more than 66% of insurers who used predictive analytics tools in their claims processes experienced a considerable reduction in processing and underwriting costs.

Using predictive analytics can also enable insurance providers to identify potentially fraudulent claims and take measures to prevent these claims from entering the investigation process, thereby reducing time and resource wastage.

Finally, predictive data analytics can help outlier claims—large unexpected claims that lead to losses for the insurance provider. 

During the height of the pandemic, many businesses had to shut down and file for insurance payouts to keep their employees on the payroll. With predictive data modelling, insurance providers can predict these kinds of events in advance and handle the claims accordingly.

Identifying potential customers

As we’ve established above, traditional insurance markets are maturing, and insurance providers are experiencing low levels of policy buy-ins. 

Insurance providers need to stay profitable in this market and for that, they need to look to newer markets or even niche customer segments in the existing markets; predictive analytics can help insurers do this.

Using the available data, predictive analytics can identify behavioural patterns of target demographics and produce actionable insights, which insurance companies can use to create new insurance products that suit those demographics.

Predictive analytics drive the insurance industry forward in a tough climate

The insurance industry has always been one of the slowest to adapt to changes and utilise new technologies. 

In the current business environment, however, this needs to change and the industry has to take a proactive stance, investing in tools like predictive analytics to improve their operational efficiency and profitability.

How does data analytics boost customer loyalty in the insurance industry?

The world is still in recovery mode amid the evolving global health crisis. Interest rates are lower than ever to encourage loans and investments, however, this has also resulted in minimal returns for paid-in premiums. 

The insurance sector is seeing an unprecedented decline in product penetration growth—especially in mature markets. The latest statistics have revealed that the fee structure for financial advisors presents a conflict of interest that can harm consumers, especially in Europe, the US, and Australia. 

This has industry analysts and experts hunting for new solutions. The only viable solution seems to be adopting a more customer-centric approach to insurance.

The path to achieving that involves big data. Big data can provide the insurance industry with plentiful, unprocessed, raw information, and data analytics platforms can process it into the insightful data that is needed to incite change. 

Does having a loyal customer base solve the challenges facing the insurance industry? 

Data shows that customers who are loyal to their insurers cost less to serve, stay longer, buy more, and recommend their insurance provider to family and friends. The survey, however, also reveals that insurance companies find it hard to build customer loyalty. 

The same set of data processed through different filters allows us to understand that the main reason behind this is the lack of interaction between insurance providers and their customers. 

Enhance brand image and improve customer satisfaction

Data analytics provide insurance companies with the insights they need to build all-inclusive policies. This means that customers can get tailor-made policies that fit all their requirements instead of purchasing multiple plans from the same provider. 

Data analytics can also help insurance companies optimise their communication channels to have better interactions with their clients. 

Knowing the different demographics that form your customer base and noting their preferred mode of communication, whether it is through a website, in-person, phone call, or video chat can help build relatability. 

In short, interaction is the pillar of building customer loyalty and data analytics is its facilitator.   

Solve fraud issues and keep premiums low

Fraud is one of the biggest issues faced by the insurance industry. Statistics suggest that at least one in ten of every claim filed is fraudulent. Considering the number of policyholders these companies serve, this can reach a staggering number. 

The result of this is an increased premium for the rest of your client base. With data analytics, however, it is possible to find these cases, resolve them, and prosecute the culprits swiftly before it causes widespread effects.  

Big data solutions such as social network analysis and telemetrics can be used to achieve this. 

Leveraging these solutions pays off because keeping your premiums low is a guaranteed way to keep customers satisfied and loyal. 

Accelerate settlement cases and streamline customer payouts

Data analytics can also be used to speed up settlement cases. The main reason lawsuits and claims take a long time to settle is because of the large amount of analysis that needs to be done. 

Data analytics allow firms to check the claim, analyse it, and access the customer’s claim history instantaneously. This can increase the speed with which a firm can give customers their payouts.  

Innovations and solutions geared at improving customer interactions 

The rise of online aggregators or comparison sites, to use the more popular term, has led to an even greater decline in the interactions between customers and service providers.

Technology has, however, allowed many companies to redefine their image and service as insurance providers in order to build meaningful relationships with their clients and, in turn, develop loyalty. 

These changes and innovations would not have been possible without the insights gleaned from data analytics and the advances made in data modelling techniques. 

Advances that include:

• Insurance companies investing in wearable devices that can help improve worker safety. 
• Funding smart home technology to minimise flood damage.
• Gamifying safe driving to stimulate consumer engagement. 
• Stolen vehicle tracking and recovery services. 

Building insurance companies that are customer-centric with data analytics 

With the information gathered from social media, mobile data browsing history, and purchasing history, companies in the insurance industry can gain a more personal understanding of their clients. 

Equipped with this information, they can find swift solutions to process and service issues, providing the customer with an easier and more wholesome experience. 

When coupled with the ability to build insurance plans and policies tailor-made for individual customers, we can confidently say that big data and data analytics together can help increase customer satisfaction and build loyalty.

What can data analytics do for the tourism industry post-COVID-19?

The Coronavirus has dealt a heavy blow to the global economy. No industry, however, has suffered as much as either aviation or tourism. 

Tourism makes up to 10% of the world’s GDP and creates 1 in every 10 jobs. It’s the livelihood of many people around the world, and developing nations are especially reliant on the revenue it generates.  

It’s not hard to see, then, how travel bans, quarantines and social distancing can be major roadblocks to travel experiences and how people continue making a living. 

It is yet to be seen how our spending ability and habits have been affected in the long run by the pandemic and we can’t say for certain how it will end—either we cure it completely or learn to live with it. 

Regardless, we can expect a post-vaccination boom in travel especially with the introduction of initiatives like vaccine passports. In the new normal of travel and tourism, how do tools like data analytics help us forge a new way forward?

What data can the tourism industry benefit from and how is it being used?

In today’s global community, data is an undeniable currency. On the subject of tourism, what data would be valuable to the industry and how can it be used?

On a macro level, the GDP generated by the industry and which sectors contributed to it, and to what degree this took place, is invaluable.

Governments, tourism boards and travel agencies can use this data to identify key areas they need to invest in and where they need to improve their infrastructure and services. 

It can also help them identify what’s being done right so critical investments are only funnelled into the right areas.

More important still is understanding the preferences and habits of various demographics. By knowing a visitor’s country of origin, how long they stayed, how much they spent, and which places they visited, tourism companies can create and offer tailor-made experiences. 

While this is being done to some extent at the present, we can expect COVID-19 safety considerations to be factored into travel packages and experiences in the new normal.

By understanding each class of travellers, companies can develop targeted marketing campaigns, identify which countries to focus on, and allocate their marketing budgets accordingly.

Big data also allows governments and companies to understand where and how they need to improve so they can offer tourists safer and more unique experiences. It can also measure success and failure and in the long run, make greater strides in sustainable tourism. 

How can data analytics boost the tourism industry post-COVID-19? 

When the world settles into some semblance of normalcy, it’s highly likely that most people won’t have the same habits and preferences as they did before. While we can expect an increase in people wanting to travel, it’s reasonable to anticipate that their requirements and priorities will change. 

Travellers might, for instance, opt not to travel to developing countries given the relative lack of medical facilities and emergency services. It is also quite possible that for years to come, tourists will seek destinations closer to home. 

Popular destinations in Asia, Africa and South America may find themselves in a predicament where they need to improve critical infrastructures such as transportation, sanitation, healthcare and other public facilities to convince tourists that these locations are popular, safe, and rewarding destinations to sojourn to. 

Data analytics can also help us identify key areas that need to be improved or services that need to be introduced. 

It can also give policymakers and the spate of travel and tourism companies insight into emerging trends and help the industry prepare and respond better to a post-COVID-19 world. 

Leveraging the right insights will speed up the recovery of the tourism industry  

Given the potential of big data and the range of analytics insights we have access to today, we can expect the tourism industry to be back on its feet sooner rather than later. 
The lean efficiency that is the benchmark of the industry is only possible with the insights offered by data analytics, however. It’s even safe to say that data analytics and data modelling techniques are the cornerstone of the industry’s recovery and retooling in the new normal.

Driving banking to the future with banking analytics

Banks are one of the oldest institutions in human history. From the very first civilisations to the present day, banks have always existed in some form or another and have been instrumental in advancing the economy, driving innovation, and nurturing businesses.

Although banks have existed for many centuries, their fundamental principles have remained the same; accepting deposits from surplus and lending to fill deficits. 

Along the way, they have adopted the latest technologies to support their core functions. From the first banknotes to modern, contactless payment solutions, banks have been at the forefront of financial evolution.

That said, ever since the advent of the internet and the birth of the information age, banks have come under significant pressure to modernise their processes and operations to meet changing consumer preferences, comply with complex regulations, and tackle sophisticated fraud schemes.

The innovations that have caused a fair share of challenges have also created the solution to tackle them, and that solution is banking analytics. With advances in computer technology, artificial intelligence, and machine learning—modern data analytics solutions can help overcome the contemporary challenges that banks are facing.

In this post, we will explore how banking analytics can power the banks of the future and help them overcome the challenges ahead.

Why is data analytics important in banking?

Through judicious use of data analytics, banks can improve customer service and foster innovation, which is crucial for retaining customers and eventually raising revenue while cutting costs. 

Thanks to data analytics, banks can personalise experiences for customers. Analytics platforms can break down and segment customer data to create detailed profiles on each customer and banks can use the information to provide personalised services to new and existing customers. 

Banks can gain better insight into customer behaviour by analysing actions, like channel transactions, to better understand how customers operate in real-time. The insight into customer actions allows banks to tweak existing services to better suit their customer’s needs, which improves retention. 

Data analytics can also reduce operating costs related to compliance management, fraud detection, and credit risk.

A worthy adversary to banking fraud

For as long as banks have existed, fraud has plagued them. While banking fraud in the past included disguises and counterfeit notes, modern fraud schemes have taken it up a notch.

With the power of the internet and some clever pieces of technology, modern fraudsters can scam individuals out of thousands of dollars without ever leaving their sofas. In 2020, global losses from payment frauds involving banks reached $32.39 billion and are estimated to reach $40 billion by 2027.

It doesn’t end there, banks now have to deal with more elaborate and sophisticated identity theft attempts and more accurate counterfeit notes. Identity theft was the most common type of bank fraud in both 2018 and 2019.

Banking analytics, however, can help banks ensure maximum protection against these frauds. For example, some leading banks have been using anomaly detection algorithms, powered by artificial intelligence, to detect potentially fraudulent transactions across their payment systems.

Modern biometric authentication systems and facial recognition systems are also known to be effective against identity theft when implemented with machine learning to detect abnormal activities in customer accounts.

A powerful credit risk analysing tool 

The global financial crisis of 2008 helped banks realise the need for more robust credit risk assessment tools to minimise risk exposure. They found out the hard way that conventional risk assessment techniques do not always produce accurate or reliable results.

This prompted banks to look for more innovative and efficient credit risk management processes. Data analytics has emerged as a powerful tool to help banks power more efficient ways to manage their credit risks.

Banks can build more accurate and comprehensive financial profiles of their prospective borrowers using banking analytics tools. These profiles help predict and reduce instances of loan defaults.

These tools can also help banks lend to the correct type of borrowers, reducing credit risks.

Powering a novel approach to regulatory compliance

Banks are one of, if not, the most important institutions in an economy. This is why they are governed by some of the most complex and stringent regulations. In addition, the regulatory landscape is in constant flux with regulators introducing new guidelines and amending existing ones consistently.

To navigate this dynamic regulatory landscape, banks need to be on top of every single regulatory change, which can be hard to achieve with your teams. By nature, people are prone to occasional error, which makes regulatory compliance an arduous task.

Thanks to advances in data analytics, machine learning, and artificial intelligence, banks can automate their compliance process. These RegTech solutions can power proactive compliance processes by automating compliance documentation, regulatory horizon scanning, and compliance monitoring. 

Banking analytics is the future of banking

As the lifeblood of an economy, banks across the globe need to adopt disruptive technologies to combat contemporary challenges. Banking analytics is the tool banks need to prepare for the future.

What data analytics can tell us about unemployment

Before the advent of the internet, instant connectivity, and big data, governments and organisations dedicated to fighting poverty didn’t have access to real-time analytics platforms to decipher their data in a meaningful way. 

Today, analytics allows us to know that there are over a million people who are unemployed in Australia, with the unemployment rate surging to 7.4%; the highest since the recession of the 1990s. 

Given the treasure troves of data available to us, we know what people like to eat, how they spend their time, which communities they belong to, what they watch and their opinions—statistics and data, which, at first glance, would seem completely unrelated to unemployment. 

Alongside the new data science modelling techniques that are available, however, we can use even seemingly irrelevant data like this to identify and categorise similar groups of people, behavioural patterns and preferences to understand unemployment with a broader scope. 

Diving deep into socio-cultural issues that contribute to unemployment 

Because of the internet and nearly all people having access to it in some form or the other, it is now possible to break down human behaviour into data sets and make correlations between our preferences and behaviour. 

For example, we could say that in the US, certain urban communities grow up in areas where violence is commonplace, where peers influence teenagers and young adults into crime. Once someone has a criminal record, finding gainful employment is virtually impossible. 

Statistics show, however, that though there is a relationship between crime and unemployment, it is insignificant when it comes to crimes like burglary, larceny and robbery. It can also be inferred that the link between unemployment and crime becomes much weaker during recessions. 

Analytics make it possible to make more sense of this data and may illuminate potential strategies governments can execute to provide vulnerable youth with gainful employment.

Rectifying unequal distribution of resources as a means to fight unemployment

Even without data analytics, most people today could make an educated guess that for the most part, resources are distributed unequally between cities and rural areas—the differences in schools, universities and jobs are quite obvious. 

This opportunity gap between people who are geographically, socially and economically displaced is more jarring than ever. 

Analytics can help us understand to what extent and further distinguish aspects of our social framework that lead children and young adults to grow up into unemployment. It can, for instance, help us fix certain issues in the school system, rectify resource distribution issues and find ways to keep children interested in education, or if necessary, pursue specialised vocational training instead. 

Data analytics is a great tool, therefore, for governments and authorities to understand and help certain communities develop in a meaningful way, not with just the goal of getting a job, but building a career.  

Clearing misconceptions about unemployment 

When we consider unemployment, it’s important to have relevant real-time analytics about the number of people that enter the job market each year, their level of education, skills and the rate of new job creation, to name just a few of the most relevant factors. 

Without big data and analytics, authorities can’t keep track of these statistics and find a lasting and viable solution to a problem as complex as this. 

Analytics can also help gauge the success or failure of any solution that is implemented. It can help us determine whether it is a long-term or a short-term solution and whether it can be replicated over different demographics and if so, with what modifications.

Can analytics help us solve the global unemployment problem?  

Given the rich range of insights analytics presents us with today, understanding a complex and multi-faceted problem like unemployment and breaking it down to its bare bones is much easier today than at any time in the past.

If leveraged effectively, data analytics and new data modelling techniques will give governments and NGOs the tools they need to provide a lasting solution to the unemployment question.

Ridesharing apps and how they use data analytics

The wheel was one of mankind’s most important inventions. Ever since our ancestors started using wheels, our transportation methods have evolved rapidly. 

The wheel and subsequent inventions thereafter have transformed transportation into what we know it to be today.

In recent years, one of the most interesting evolutions in the transportation industry has been the rise of ridesharing apps. Startups like Uber, Lyft and Lime have given us more options to commute comfortably around the city.

Beyond just disrupting the industry, these services have taken the hassle out of hiring taxis and brought an entire fleet of transport options right into the palm of our hands. Due to their ease of use and intuitiveness, ridesharing services have exploded in popularity and are now offering services across every continent—except Antarctica.

In the case of Uber and Lyft, their popularity has given them the unicorn status—startups with a valuation of over $1 billion. As of November 2020, Uber was valued at $78 billion and Lyft is valued at $24 billion.

All of this success is built on a strong foundation laid by data analytics. It is key to the effectiveness of these ridesharing apps. The entire business model of these apps is based on the big data principle of crowdsourcing.

In this post, we explore how ridesharing apps are using data analytics to power their services.

Ridesharing apps use data analytics to determine prices

One of the key benefits of ridesharing platforms is that passengers can get an idea of how much their commute will cost even before they hire a taxi. Commute prices are not fixed; these apps use advanced algorithms to determine the price of each ride.

Prices for the same commute at different times of the day will cost different amounts. This is because traffic conditions change throughout the day, and ridesharing apps use real-time traffic data to factor these conditions into commute pricing. 

This practice is called surge pricing.

Prices are affected by demand as well. In most cities, commute prices at peak times are likely to be higher than off-peak times.

Surge pricing is not only beneficial to commuters but to drivers as well since it encourages drivers to get behind the wheel when the demand is high and offer more rider-friendly rates when demand is low.

Analytics connects commuters to available drivers

When a passenger opens Uber, Lyft or any of the other ridesharing apps and requests a ride, the app collects their location data through the GPS sensor in their smartphone. 

Once the data is collected, it is sent to the central hub where commuters are connected with drivers.

Ridesharing apps track the location of drivers constantly. Once a ride is requested by a passenger, their location data is compared to the driver’s location, and a driver from a nearby location is assigned the trip.

Most ridesharing apps also allow passengers to share their rides with other passengers; 

Uber, for example, has a feature called UberPool, where passengers can share a trip with other passengers travelling along the same route. The feature uses pickup location, drop location, and route details to pool together passengers, allowing them to get to their destination at a lower cost.

Data analytics powers ride rating system

All ridesharing services feature a pretty important ride, rider and driver rating system. 

Drivers can rate passengers and vice versa, allowing them to make informed decisions about who they want to share their commutes with.

The apps decide which drivers to assign trips to based on the aggregated ratings across their trips. If ratings fall below a threshold determined by the ridesharing platform, drivers are not offered rides in the future.

In addition to the regular rating system, drivers are rated based on their acceptance rate—the percentage of how many trips they accept out of the assigned trips. These platforms use algorithms to favour drivers with a high acceptance rate. 

The success of ridesharing companies is driven by data analytics

Today, ridesharing companies have simplified how we get to and from various destinations. Due to their simplicity, these apps are used by millions of people across the globe.

The driving force behind the success of these platforms is data analytics, as they use big data for everything from determining prices to assigning drivers to commutes. 

Transportation, as we know it, has been changed forever by the data we’re generating and collecting.