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Tag: artificial intelligence

Blog Informative

Preparing for Your Machine Learning and Artificial Intelligence Transformation

Machine Learning (ML) and Artificial Intelligence (AI) has use cases across multiple industries and business functions, but how do organisations ensure that they get the most from the implementation of ML and AI? Companies who reap the rewards of these tools usually undergo a common process to prepare for the changes that come with the application of new technologies and practices.  

 

This post aims to briefly cover the important steps that organisations undergo before realising the many benefits of Machine Learning, in other words, these are the critical aspects in a strategy for organisations starting their AI transformation journey.  

 

First: Where Are You? 

How will you know where to go if you don’t even know from where you are starting? 

 

 

Organisations wanting to implement Machine Learning and AI solutions need to clarify what their AI goals are – realistically. By assessing current skills of employees (both in IT and other departments), facilities or infrastructure, available budget, and free time to complete end-to-end ML solution delivery, organisations can develop a strategy. This strategy will include aspects such as: 

 

State of Data 

Organisations will need to assess current processes related to data – how is it being collected, cleaned, organised, accessed, and processed? In order to for ML models to produce accurate and useful outcomes, data needs to be in good shape and properly supported. 

 

State of Infrastructure 

85% of organisations are ill-equipped to begin their AI transformation. Artificial Intelligence and Machine Learning models process large amounts of data in real-time, therefore the infrastructure supporting it needs to have fast and modern processing abilities. 

 

Company Culture 

With the implementation of a revolutionary technology, employee mindsets need to be on-par. Leaders within the organisation need to be able to shift their decision-making to embrace data-driven thinking. Teams need to be open to adopting this new technology as well as educated about its usefulness to them (not just to the company at large). 

 

Internal Expertise 

Once experts have delivered the sought Machine Learning capabilities, there may need to be internal team members to continue maintaining the quality of data and observing data patterns for ongoing success. Most organisations do not have the internal capabilities to ensure ongoing success of the technology. This means that organisations need to consider support from external experts (or Managed Services). 

   

Second: Goals 

Now you know where you are, where to next?  

In order to determine what needs to be implemented, organisations need to decide what they want to achieve from the technology. A successful AI and ML implementation journey includes setting goals – what needs to be optimised, automated, or improved? Once these goals have been determined, the most important aspect about bringing them alive is: ensuring the process behind achieving these goals is optimised, rather than the goal (or job) itself. This may sound confusing at first but ultimately it means automating the process (for example, segmenting customers into target groups for marketing) rather than automating the job (the marketing co-ordinator’s job of deciding on targeted campaigns).  

  

Third: Enterprise Support 

 

Just as implementing AI and ML in an organisation cannot be done by one person, the AI strategy mapping cannot either. Keeping the core goals of the company in focus whilst determining AI and ML goals requires interdepartmental planning and collaboration. This usually means leaders from different teams and departments get together as do data engineers and data scientists (internal and external) in order to establish the state of all processes across the company for better goal setting.  

 

Final Notes 

An AI and ML transformation journey can only begin with a deep and guided company strategy – including the current state of things and the goal state of things – and this needs to be all-encompassing and all-inclusive in order to prepare an organisation properly for the start a new technological achievement. Skipping this step will likely result in time, resources, and money wasted – leaving all those involved feeling confused and frustrated.  

 

Being such a vital step of reaching AI and ML goals, the company’s AI and ML strategy needs to be done well. This is best achieved by ensuring those in charge of the strategy are experienced and experts. Intellify is Australia’s leading AI and ML consultancy – delivering end-to-end machine learning solutions from strategy, to enablement, to deployment, and ongoing support. Contact us to begin your AI transformation and stay ahead of the curve.   

AWS In the Media News & Articles Why Choose Us

Intellify – now an AWS Advanced Tier Partner

We are proud to announce that we are officially APN Advanced Tier Partners
Intellify is dedicated to constantly improving and upgrading our skills to ensure our consultants are always up to date with the latest that AWS has to offer. Watch the video below to find out more about what this means for your organisations and for our customers.

Contact us here to learn how our award-winning Machine Learning solutions and support can optimise your business functions.

Blog Use Cases

Use Cases of Machine Learning in the Telecommunications Sector 

 

Machine Learning (ML) and Artificial Intelligence (AI) are becoming a key implementation in every industry with high value and efficiency being provided across the board. The telecommunications sector is no exception and is at the forefront of adopting Machine Learning to optimise operations, increase revenue, transfer and visualise data, enhance customer relations, and improve marketing and sales strategies.  

 

Data transfer, exchange, and analysis is key in the telecommunications sector with the amount of data increasing every day. This is why all systems and methods relating to data need to be relevant and accurate.  

Below are a few of the use cases where Machine Learning and Artificial Intelligence have provided benefit in the telecoms industry: 

 

Product & Service Recommendation Engines 

 Recommendation engines in many industries come up in our digital life. It would not be effective to market product recommendations without taking customer preferences into consideration. By analysing historical customer data (their behaviour and preferences), Machine Learning algorithms are able to predict which products a particular customer will most likely purchase.  

This is done through filtering. Collaborative filtering makes assumptions about a customer’s preferences based on behavioural similarities to other customers (assuming that people with similar profiles have similar interests). Content-based filtering makes assumptions based on previous customer product history – recommending items similar to those they have already purchased. Customers are more likely to upgrade or repurchase products that align with their needs or preferences. This is why telecom companies have implemented this ML method to increase revenue.  

 

Anomaly Detection & Fraud 

 Due to the fact that the industry attracts a significant number of users every day, this brings high chances of fraudulent opportunities. Fraud, such as fake profiles, theft, illegal authorisation, and account cloning, have a negative impact on the customer-company relationship.  

This is why telecommunications companies are implementing unsupervised Machine Learning algorithms to spot abnormal characteristics or activity in customer behaviour. 

By analysing past data of customer behaviour, ML algorithms are able to visualise and present these anomalies to analysts in real time. This is especially efficient as it allows telecommunications companies to alert their customer of suspicious activity almost immediately thus protecting the customer-company trust relationship. 

 

Customer Segmentation for Effective Marketing 

Successful marketing in any industry relies on accurately segmenting and targeting groups according to their preferences and characteristics.  

 

The key to success for the telecommunication companies is to segment their target markets and target marketing content accordingly to each group. This golden rule is relevant to the various areas of business. Machine Learning algorithms can create highly accurate customer segments based on shared customer characteristics. This allows telecommunications companies to better strategise and reach customers with more relevant marketing material (and increase their chance of a sales conversion)  

 

(Did you know that ML customer segmentation can be done in real-time. As customers change preferences and patterns, they are re-segmented to different groups based on whichever characteristics are most prominent. This means targeting the customer with irrelevant marketing material is less likely to happen – and saves time for those who once needed to re-segment groups manually).  

 

 

Optimised Product Development 

Companies in the telecommunications industry need to provide their customers with products that satisfy their needs and wants in order to continue to keep customers loyal and to generate revenue. Machine Learning algorithms can analyse data across different departments to ensure customer feedback is taken into account to provide the most customer fulfilling products are created. By analysing data such as customer feedback, marketing intelligence, and product stock, ML algorithms can present the most data-driven product suggestions for development. 

 

Conclusion 

Data science and its benefits in the telecommunications industry is not a new occurrence. However, Machine Learning has taken efficiency within the industry to the next level. Those companies that have implemented ML in their processes are experiencing fewer issues to resolve, control or occur from happening at all. 

With the right partner and expertise, telecommunications companies are experiencing these benefits for themselves. Intellify is AWS’ Partner of the Year in Data, Analytics, & Machine Learning and has worked with telecommunications companies in the past on bringing Machine Learning capabilities and culture to their organisation. Contact us to learn more about how we could help you do the same based on your own requirements.   

 

Blog Use Cases

Predictive Maintenance in the Manufacturing Industry

 

The increase in the use of Machine Learning (ML) and in the levels of automation in manufacturing has naturally led to improving asset productivity through data-driven insights that were not possible before. Now we see predictive maintenance taking the place of the “run to failure” approach where asset maintenance is scheduled once a failure in the system is observed. Predictive maintenance is a set of processes that reduce maintenance costs and prevent extra time being wasted on repairing assets – particularly in dependent machinery systems.

In order to detect changes in the system that point to potential “health problems” in machinery, pattern-recognising machine learning models are deployed. Manufacturing companies are using predictive maintenance software to reduce unplanned downtime and maximise profitability. Machine learning predictive maintenance is unique because it not only predicts impending failure but produces outcome-focused recommendations for operations and maintenance from analytics.

 

How Does This Work?

The effectiveness of ML predictive maintenance models relies on the health indicators of the machinery. Historical data from these health indicators (features) is analysed by ML models. The ML models then compute another indicator which describes the machinery’s health and an estimated remaining lifetime.

 

The below are the base components for predictive maintenance to be carried out:

Data Sensors – sensors that collect data are installed on the machinery

Data Flow – a communication system allows machinery data to flow to a data storage centre

Data Storage – a central hub stores, processes, and analyses the machinery data either on the premises or on the cloud

Predictive Analytics – algorithms are applied to the processed data, patterns are recognised, and insights in the form of dashboards and alerts are created

Cause Analysis – corrective action is determined based on machinery health insights

 

 

When is Predictive Maintenance Being Used?

Manufacturing companies (and many other companies across different industries) use ML predictive maintenance in a number of ways. Manufacturers of machines and machinery parts use predictive maintenance to analyse and monitor the condition of motors and their levels of wear, power consumption, and productivity. Predictive maintenance is also being used to minimise production errors and reduce waste. ML models can predict when the number of faulty products is likely to exceed a set maximum percentage and give causes for the expected problems with the products. Manufacturers are also using ML predictive maintenance models to predict issues across the factory floor: by linking all monitored sections with installed machine sensors, security cameras, worker equipment, and workstations.

 

Final Notes

Manufacturing organisations who have already implemented ML into their daily functioning are seeing great benefits of predictive maintenance – an increase in information delivery speed, an increase in usefulness of information, and decreases in costs. The ML predictive analysis has allowed organisations to make smart decisions based on their own data. This results in a complete solution across operations, engineering, and maintenance. Machine learning predictive maintenance plans give managers unmatched insights into operations that can lead to optimised safety, efficiency, and decision-making. Taking a predictive maintenance approach to asset performance and reliability means manual root cause analyses are no longer needed; instead ML models will consider an entire history of asset performance and failures to identify signs of impending problems in advance.

Contact Intellify, the AWS Partner of the Year 2019 in Data, Analytics, & Machine Learning, to learn more about how your organisation can benefit from machine learning & artificial intelligence. We provide end-to-end services, helping companies reach their full potential by leveraging data proactively.

Blog Use Cases

Implementing Machine Learning in the Energy Industry

 

 

Artificial Intelligence (AI) and Machine Learning (ML) are both hot topics in business these days. These advanced analytics tools have been introduced into businesses across many sectors – from retail, to finance, to marketing, with one of the less publicised yet important sectors being the energy and utilities sector. 

 

The power of Machine Learning, data science, AI, and predictive models has made many renewable energy companies more efficient across business functions – lowering costs, increasing returns, and making more accurate predictions. 

 

Terminology 

Before getting in to how Artificial Intelligence and Machine Learning can benefit energy companies, allow the below section to provide more clarity on the terms.   

Artificial Intelligence (AI) refers to any machine or model that can conduct human-like activities.  

Machine Learning (ML) is related to AI, however they are certainly not the same thing. 

AI refers to computer-generated intelligence in machines; Machine Learning allows for some sort of artificial intelligence by using statistical techniques.  In other words, Machine Learning algorithms allow a machine to train itself based on datasets to perform a task more efficiently. 

 

Artificial Intelligence and Machine Learning in the Energy Sector 

Below are 3 use cases of these data analytics tools in the energy industry:  

 

Forecasting Energy Demand  

When it comes to delivering electricity to customers through complex power networks (grids), there needs to be enough electricity or energy available to match the demand for it. Otherwise blackouts and system failures may occur. 

 

Machine Learning algorithms can determine demand by looking at past data of daily energy consumption changes for individual customers over time. From this data Machine Learning models are able to generate very accurate energy demand and consumption forecasts. These predictions can be used by energy & utilities companies to save time, optimise operations and every storage systems, and prevent customer dissatisfaction.  

 

Predictive Maintenance for Power Systems 

In addition to matching energy demand with energy production, Machine Learning is a beneficial tool to ensure the consistency and health of power grids. In the past, massive blackouts and power cuts have occurred with no warning to energy companies of upcoming predicted faults in their systems or even warning about the power cuts currently occurring. Machine learning algorithms can implement predictive maintenance to predict when assets (power lines, stations, and machinery) are estimated to fail. These predictions are made from analysing data – collected from sensors on power lines, boxes, and machinery – against a timestamp.  This helps ascertain when assets are predicted to fail and can be used to predict the expected remaining useful life of assets. 

 

This is useful information for energy companies for saving time (replacing or fixing those assets predicted to fail before they are completely broken and have to re-ordered/replaced etc.), saving costs (allowing companies to optimise maintenance activities based on accurate predictions), and reducing customer dissatisfaction (fewer unexpected asset failures should result in more consistent running and provision of power). 

 

 

Efficient Energy Storage  

Besides being beneficial for traditional coal and gas energy companies, renewable energy companies can too reap the benefits of Machine Learning tools. A commonly known issue with renewable energy is its sporadic availability. When there is no wind or sunshine for a while and therefore not enough energy production or, on the other hand, too much energy produced for the amount of consumption (causing the surplus energy to go to waste), it is essential for energy companies to optimise energy usage and storage.  

Machine Learning solutions can be used to predict energy usage and manage storage decisions. For example, ML models can manage electricity shortages by briefly cutting off the supply for electricity coming from the main grid, while using stores of energy from regions. This reduces unused and wasted energy supplies. 

In the case of saving and storing energy, Machine Learning can generate forecasts for weather, energy production, and electricity demand to optimise energy usages on “overly productive energy days” and thus reducing the need for cutting off main grid supply.  

 

The Future of Machine Learning and Artificial Intelligence in the Energy Industry 

For developed countries with “green” goals for an environmentally-friendly economy with resistant and consistent power supply is of top priority, Machine Learning algorithms that predict demand, improve performance, reduce costs, and prevent system failures are a clear step in the right direction.  

 

With the right partner, your energy company can reap the benefits of Machine Learning and get accurate, insightful, and actionable information out of your data to more accurately forecast energy demand, predict asset maintenance, and more efficiently store energy. Save resources, time, and costs – contact Intellify to hear how we have worked within the energy industry to do just that. We can help you achieve your Machine Learning and Artificial Intelligence requirements.  

Blog Customer Segmentation Use Cases

Customer Segmentation & Personalisation with Machine Learning

 

Customer segmentation is a method of dividing customers into groups based on their shared characteristics such as age, gender, interests, or purchase habits. This is important for allocating company resources and targeting clients effectively. Customer segmentation influences mainly sales and marketing decisions in organisations. It allows marketers to tailor their campaigns to different target groups for promotional, marketing, sales, and product development strategies. Personalised marketing and sales efforts that are based on well segmented customer groups have much higher conversion rates and therefore impact an increase in revenue. 

 

Clustering 

Even though customer segmentation is such a vital part of organisational decision-making and machine learning is advancing rapidly, very few companies are using automation processes to optimise their efforts in customer segmentation. Organisations that are using machine learning algorithms in their customer segmentation efforts often use “clustering”. 

Clustering is a machine learning technique for identifying and grouping similar data points (e.g. customers) together. The objective of clustering algorithms is to ensure that the data-points in the same group are very similar, and data-points in different groups are extremely dissimilar. This process is “the hard part” that  42% of marketers in the USA and UK do not complete due to lack of employees, time, and funding, which prevents them from reaching their personalisation goals.  

 

Once customers, stores, or locations have been segmented into groups, managers can make decisions about how to target each group most appropriately based on their differences. For example, specific segments may be optimal targets for inbound marketing campaigns such as cross-selling and others may be better suited for outbound campaigns such as email messaging. Thanks to machine learning models, companies no longer need to spend ages defining segments and allocating customers to them. Now, their time can be used for understanding who will be best served through which channel, with what message, and at what point in time. Companies can now focus on how to maximise the impact of current capabilities and resources such as available channels, agents, content, and offers!  

 

In addition to simply automating the grouping process, machine learning can impact the “contacting” process. Machine learning models will learn (based on each customer interaction) whether the chosen communication channel, content, offer, and the point in time were “right” for a given customer. Based on the customer interaction with campaign touch points, machine learning models can automatically predict what engagement mode is most likely to delight each individual customer. The more data there is, the more accurate the models will be. Machine learning models do not even need to base their assessments on data from current campaigns – historical data from customer engagement with previous campaigns can be used to train the models.  

 

 

Other Benefits of Doing Customer Segmentation with Machine Learning 

 

Simpler & More Precise Segments  

These days customers cannot only be segmented based on their demographic or purchase behaviour data for effective segmentation results. These segments are simply too broad and deliver only suboptimal conversion rates. Organisations are now including more advanced additional criteria to ensure better segmented groups. Specific data from the customers’ sales journey is included such as whether they attended an event, clicked through to the company website, clicked on a product, and whether they bought the product or not. The desired criteria to include is always changing and often growing. If managed only by humans, it can quickly become unmanageable and inaccurate. Allowing machine learning models to do this process for you, ensures your customer segments are always optimised, saving employee time and reducing the organisation’s costs.   

 

Changing Segments 

Decisions to change the segmentation criteria to include different characteristics have perviously been a challenge. Manually reallocating customers from their initial segment to another requires constant monitoring and shifting. Machine learning models automate this process and update and shift customers from one group to a more relevant group in real-time. In essence, the customer segmentation process does not need to be time-consuming, expensive, and manually adjusted, thanks to the implementation of machine learning clustering in this process.

  

Concluding Thoughts 

There is no longer a need to manually allocate customers to segments. With machine learning, each customer is their own segment, defined by any chosen criteria that organisations deem best. When it comes to customer segmentation, machine learning algorithms are highly effective for personalising the customer experience and leading to conversions. 

 

If you want to learn more about how Machine Learning can best be applied in your organisation, contact us to find out you could be saving time, money, and resources.  

Blog Use Cases

Supply Chain Optimisation & Machine Learning

Although it is not a media-attention-grabbing industry, the supply chain and logistics management industries have been in leading machine learning development industries over the last ten years. New technical developments have allowed supply chain and logistics organisations to leverage their data in ways that is decreasing expensive machinery failures, exceeding customer product and service expectations, and generally increasing their long-term return on investment.  

 

It is critical to have solid foundations when it comes to supply chain planning – one of the most important activities in supply chain management. The implementation of machine learning in the supply chain decision-making process has allowed for significant optimisation. Supply chain teams are now able to balance demand and supply and optimise the delivery process due to machine learning data analysis and intelligent algorithms. Machine learning in supply chain and logistics management decreases the amount of manual work that needs to be done by humans – large data sets can be automatically analysed without any natural human error. 

 

Here are a few of the areas of supply chain and logistics management where machine learning models are being implemented for optimisation: 

 
Inventory Management 
 

Both under-stocking and over-stocking are common problems in supply chain management that can ruin even the most efficient supply chain strategy. Machine learning algorithms can monitor the stock levels in a warehouse and take into account more factors than typical forecasts – this can improve inventory optimisation, particularly in businesses with multiple distribution locations. This is useful for reducing transport costs (transporting a more accurate amount of stock, fewer times) and keeping inventory at a “comfortable” level (idle stock levels can be decreased based on more accurate machine learning predictions of future demand). By analysing current inventory data, inventory management can be improved to ensure optimal business performance and allow workers more time to focus on product quality and customer experience. 

 
 Warehouse Analysis 
 

Machine learning has enabled computers to “see” and understand images and videos – this is called Computer Vision (CV). This tool enables warehouse automation. Product barcodes can be processed, warehouse perimeters can be monitored, and employee activities can be followed: preventing safety violations and theft or trespassing.  

 

In addition to CV, machine learning is enabling robots to take on mundane tasks that would usually be assigned to a human. For example, machine learning robots can pick ready goods to send to customers, can carry up to 500kgs as they make their way around with sensors to avoid collisions, and can be summoned by workers at any time. This technology is improving team productivity and efficiency in the warehouse while reducing the risk of human injury (from lifting, managing, and carrying heavy goods) and human error – like a parcel being sent to the wrong address.  

 
Logistics Route Optimisation 
 

Customers expect their deliveries to arrive at the time and date promised to them by the company. Therefore, it is important to ensure this happens without any delays and in the most cost-efficient way possible. Delivering parcels to customers on time is no simple task – for example, delivering just 25 parcels by van has 15 septillion (a trillion trillion) route options. Machine learning models can analyse existing route options and implement route optimisation – creating a current optimal route depending on road conditions, weather, and other factors. This means companies are able to reach bigger geographic locations, reach more customers efficiently, and generate more revenue. Of course, keeping the customer satisfied too. 

 
Supplier Relationship Management 
 

When you make an incorrect decision about your organisation, your business can suffer. This also applies when making decisions about your suppliers (in the worst case, a bad supplier choice can end your business). Machine learning can make the task of selecting a supplier and maintaining a relationship a lot easier. Reliable predictions about almost every interaction with an existing or potential supplier can be provided by machine learning models based on historical data sets (such as messaging records, audits, or credit scores). In this case, machine learning can help organisations avoid selecting an untrustworthy supplier and maintain a long term mutually beneficial relationship. 

 
Workforce Planning 
 

Most industries and business sectors involve team management and planning. The same goes for the supply chain and logistics management industries. Team management processes involve recruitment, retention, employee training, sub-team management, and performance analysis and more. Machine learning solutions can be applied to these areas of workforce management. For example, machine learning models can identify key traits of the most successful employees and use that information to shortlist most ideal candidates form a collection of CVs. 

 

Research shows that employees leave their jobs due to salary less often than expected. This means that employee turnover can be decreased without increasing salaries. Machine learning predictive analytics can save companies money on hiring expenses, training costs, and overall payroll expense by leveraging employee data (employee surveys, for example). This data could provide insight as to what really makes employees happy at work. This can influence a positive work culture and increase productivity without breaking the bank. 

 
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In conclusion, machine learning has applications across multiple sectors in supply chain and logistics management. From ensuring inventory levels are optimised throughout changing demand levels over the year, to monitoring warehouse and employee safety, to moving inventory in the warehouse more efficiently, to picking the most optimal route for product delivery, selecting and maintaining a healthy supplier relationship, and to team management, machine learning can leverage your organisational data and allow employees more time to focus on customer satisfaction. Allow machine learning models to increase your business revenue and be a long-term investment that save time, resources, and costs in your supply chain or logistics management organisation now.  

 

Maintain your competitive advantage and contact Intellify for more information on how machine learning can be customised for your company to allow you to optimise all department involved in your product or service delivering.  

Blog Events

PyData Sydney – July 2019

 

 

This month, PyData was hosted by Intellify at the very slick Executive Centre in Sydney’s CBD. Although seats were limited, the room filled up quickly and was bustling with chatter in only fifteen minutes.

 

Soft drinks and pizza were enjoyed by all and the crowd of data scientists, analysts, and other machine learning enthusiasts did not need any encouragement to get their networking going!

The first presentation of the evening was from Senior Data Scientist Ammar Mohemmed. He gave an introduction into Optimisation using Python. His presentation was technical as well as practical – with lots of useful examples from real-life scenarios where price and quantity were involved. His presentation made clear that while machine learning and artificial intelligence technology is an absolute time saver and more accurate than manual human calculations, there will always be a need for human analysis from a business perspective to re-check the suggestions that the models generate.

 

 

The second presentation was from another data scientist from Intellify, Jordan Wade. He presented an algorithm that combines both genetics and neural networks – called NEAT (NeuroEvolution of Augmenting Topology). He enlightened us on how neural networks in artificial intelligence can evolve in a similar way that biological life does (except the technology is much more sophisticated in many ways). Evolutionary algorithms always heavily mirror biology, neuroevolution being no different in this respect.

 

The presenters answered questions and conversed with members of the PyData community and the evening concluded.

 

We would like to thank our venue host and all the members of the Pydata community who joined July’s meetup. We hope to see more of you next time!

For more information about Pydata and our events please click here. Follow us for more events where you can hear from practicing data scientists on their experiences machine learning methods. Proudly hosted by Pydata Sydney’s sponsor – Intellify the 2019 AWS Partner of the Year in Data, Machine Learning and Analytics.

Blog Informative News & Articles Use Cases

Machine Learning and the Finance Industry

 

 

Did you know that the term “artificial intelligence” was first coined in 1956? Even since then the world has rapidly evolved technologically. From the early days of radio, to television, to the internet, and now we have Artificial Intelligence and Machine Learning. Artificial Intelligence (AI) involves many aspects – including processing robotics and the process of automation by robotics. Due to large amounts of data and the increase in demand for understanding data patterns, AI and machine learning (ML) have become very popular (especially among large companies). When it comes to the financial services industry, thousands of organisations are using ML systems to more efficiently identify data patterns to gain target audience insights and much more. 

 

The financial services industry is full of data records. Hence the relevance of using ML to succeed in this domain. There are many use-cases of ML in the financial industry but risk assessment, fraud detection, financial advice, trading, and managing finance are the most popular (or obvious) business functions within the industry for machine learning systems. 

 

 

Risk Assessment 

To determine which customers are eligible for a credit card, banks use credit scores. However, this method of grouping customers is not necessarily efficient for business.  

Machine learning is now being used to scan thousands of personal financial records for loan repayment routines, the number of active loans, and the number of existing credit cards an individual has – to determine customised interest rates. Lenders can now determine those who are credit-worthy options if they do not possess an extensive credit history. For example, some machine learning algorithms use alternative data, such as mobile phone data, to evaluate loan suitability and customised loan rates. In addition to this, ML-powered models are objective – this removes any biases that a human credit officer may make. A vehicle lending company has experienced a 23% annual decrease in losses by implementing an AI system of loan evaluation. 

 

Fraud Detection and Management 

All organisations aim to reduce risk conditions. The financial industry (and banks in particular) take fraud very seriously – seeing as given loans are basically somebody else’s money. ML security and fraud identification systems are used to alert organisations of unusual behaviour by analysing past spending patterns across many transaction tools. A card being used in a faraway area from where it was previously used or a withdrawal of an amount of money that is unusual for the particular account, for example, are all detectable by ML systems. Understandably, these events may not be fraudulent. One of the benefits of ML-based fraud systems is that they can learn. If a regular transaction is marked as irregular by the system, it can be corrected and learn from its mistake. This allows for better informed decisions about what is flagged as fraudulent behaviour and what is not. 

 

Financial Advisory Services 

Pressure on financial institutions to reduce their commission on individual investments has increased. Luckily machines can give financial advice for a single down payment. Another use case for ML in financial advice is automated advisory – combining ML calculations with human insight to provide more efficient investment options to customers. This collaboration is key. An AI system is neither an all-knowing solution nor a simple marketing accessory; it is just as important to consider the human perspective along with the AI decisions when it comes to financial decision-making. 

  

Trading 

It is not such a recent experience that investment companies have been relying on technology and data scientists to predict future patterns in the market. In the trading field, investments are dependent on one’s ability to accurately predict the future. ML systems are highly useful in this regard: they can produce calculations from large data in a short time. Machine learning allows such systems to predict patterns based on past data (even taking into account – and planning for – anomalies such as the 2008 financial crisis). Depending on one’s appetite for risk, ML systems can suggest portfolio combinations: a person with an interest in high-risk shares depend on ML-calculated decisions on when to buy, hold, and sell. Those with a tendency towards low-risk portfolios can make decisions based on alerts from ML systems about when the market is expected to fall. 

 

 

Finance Management 

In an increasingly materialistic and connected world, managing finances can be a challenge to many people. Personal Financial Management (PFM) is one of the more recent ML-based developments. By analysing where consumers are spending their money, ML models build algorithms to help consumers make more informed decisions about their money. The model creates a spending graph which is personalised based on individual data from one’s web footprint. This may upset advocates of privacy breaching, but this is becoming a more popular way to manage personal finances as it removes the need to make length spreadsheets or hand-written budgets. From these small PFM suggestions to bigger investment portfolio suggestions, ML systems can be beneficial time- and money-saving tools to both customers and employees in the financial services industry. 

 

Machine learning is the future of many business functions within the finance industry. Soon enough it will be able to handle more financially sensitive and tedious tasks and provide more efficient solutions. The long-term cost saving benefit of ML systems are encouraging multiple financial service organisations to implement this technology. Although, currently, the implementation of ML systems in the finance industry is still in its infancy, the speed at which it is helping the industry progress predicts losses will be fewer, trading will be smarter, and customer experience will be better.  

Blog Informative News & Articles Use Cases

Machine Learning & Customer Service 

 

Machine Learning (ML) in customer services is viewed by directors, decision-makers, and by employees as a blessing to both the customer and employee experience. While technology is not yet able to perform all the tasks a human customer service representative could, machine learning in customer service teams is expected to increase by 143% over the next 16 months as more teams turn to chatbots, text, voice analytics, and other use cases. Currently, ML models in customer service are being used to both assist and replace human agents. This is primarily to improve customer service experiences and reduce the service costs of having employees handle simple requests and tasks.

Let’s explore how ML can be used to assist customer service agents; rather than completely replace them. 

 

Messaging Assistants 

We already know (and have probably experienced) chat bots are being used to communicate with customers online. However, many customers and companies feel that bots cannot handle all tasks that human agents can. Whilst still using ML technology – but avoiding frustration – “assistant” bots are being deployed to increase employee efficiency. This system allows the bots to deal with the simple questions up until the conversation becomes too complicated: then the chat is handed over to a human. Once the complex task is finished, the bot can continue with the simple details. The results of this system increase efficiency up to a point where human agents have been able to handle up to 6 co-existing chats at once. 

 

 

Organising E-mails 

Reading emails to decipher and respond to customer needs is a time-consuming task. Luckily, ML systems can speed up the process. ML technology is able to scan and tag emails to direct them to the right person along with suggested responses to the respective emails. Companies needing to respond to a large amount of emails in a short period of time have been able to halve the time customers wait to receive a response. 

 

Enhanced Customer Phone Calls 

When it comes to getting answers about financial questions, customers prefer to do so via phone call than online by a rate of 3 to 1. However, from a technical perspective, it is significantly more challenging to deploy ML voice-based communication systems. Background noise, unusual speech patterns, accents, and poor pronunciation make it hard for an ML model to translate voices into text. Yet even with these difficulties, companies are using machine learning to assist phone-call customer service agents. Conversations can be analysed in real-time with deep learning. These ML models listen to changes in volume, pitch, and can detect mimicking for classifying how customers are feelings and how calls are going. Customer service agents are given suggestions simultaneously to improve their calls based on their performance. A trial of this technology showed a 30% improvement in customer survey scores, a 6% improvement in issue resolution, and a significant decrease in requests to speak to a manager. 

 

 

 

The Future of Machine Learning and Customer Support 

New interesting uses for ML in customer service have only recently been deployed. This is why it is hard to tell how much ML will impact customer service and employment. Call centre employment is only one of many areas where this ML technology is being used (there are many retail salespeople, cashiers, and service hosts that will become freed up because up to 30% of what customer service agents do has automation potential). 

Thanks to ML advances, companies are now able to make quicker and cheaper responses to low-level customer interactions and improve more complex customer engagement experiences with human agents. This may lead to a significant increase in demand and standard of service. Millennials now have a 68% higher expectation of customer service than they had one year ago. Almost 80% of young customers expect the contacted customer service agent to already know their contact and product information. 

In the nearby future, ML in customer service will not cause an overall decrease in customer service jobs; companies may increase the number of ML assisted employees to deal with the new demand due to ML system-related efficiency and customer satisfaction.  

 

Concluding Thoughts 

Machine Learning in customer service is being used to improve the quality of service and reduce the cost of employees across industries such as finance, food, retail, and insurance. Companies now trust chatbots and ML speech-analysis systems to handle simple tasks – leading to a current significant investment in these systems. The benefits of ML in customer service is being felt directly by regular customers in a very clear and immediate sense. Customer service is a space where exciting ML applications are being deployed in across industries at a significant rate. While ML systems are being used to fully take on monotonous tasks – making it easier for human agents to deal with the more difficult issues – as they learn, these systems should soon be able to deal with more requests without human involvement. This means the current level of customer service being provided will be provided in the future at a much lower cost. This is not to say that companies will be able to spend less on this part of their business. As customers expect better service as a result of ML technology, more employees may be necessary.