Predictive Analytics in Finance: Understanding What 2022 Holds

Shaivi Chapalgaonkar | August 9, 2021

article image
The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics.

The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance.

Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process.

To be successful, organizations must have the ability to adapt to changes.

Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty.

Understanding Predictive Analytics: What is it?

Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance.

Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now.

Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics.
There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas. 

Predictive Analytics in the Financial Sector: What are the Benefits?

Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment.

It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance.

Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons:

Precise Monitoring

The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps.

Risk Alleviating

Analytics accurately helps you identify any possible threats to your business and warns you.

Enhanced User Experience

Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction.

Analyzed Decision Making

You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way.

Importance of Predictive Analytics

Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes.

But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements.

To understand the importance of predictive analytics, below are some of its use cases:

Customer first

Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe.

Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement.

Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan.

Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services.

Online Banking Made Better

Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers.

Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected.

Foreseeing Market Variations

Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors.

For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future.

Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change.

Predictive Analytics and the Future: What Next?

Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics. 

Frequently Asked Questions

Is predictive analytics is the future of finance?

Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness.

How can analytics be used in finance?

Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection.

How do predictive analytics benefit financial institutions?

Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc.

Spotlight

Pythian

Pythian is a global IT services company that specializes in designing, implementing, and managing systems that directly contribute to revenue and business success. We help companies adopt disruptive technologies to advance innovation and increase agility. Our highly skilled technical teams work as an integrated extension of our clients’ organizations to deliver continuous transformation and uninterrupted operational excellence using our expertise in cloud, DevOps, big data infrastructure, advanced analytics, infrastructure management, and databases…

OTHER ARTICLES

6 Best SaaS Marketing Metrics for Business Growth

Article | July 22, 2021

The software-as-a-service industry is rapidly growing with an estimate to reach $219.5 billion by 2027. SaaS marketing strategies is highly different from other industries; thus, tracking the right metrics for marketing is necessary. SaaS kpis or metrics measure an enterprise’s performance, growth, and momentum. These saas marketing metrics are have been designed to evaluate the health of a business by tracking sales, marketing, and customer success. Direct access to data will help you develop your business and show whether there is any room for development. SaaS KPIs: What Are They and Why Do They Matter? Marketing metrics for SaaS indicate growth in different ways. SaaS KPIs, just like regular KPIs, helps business to evaluate their business models and strategies. These key metrics for SaaS companies give a deep insight into which sectors perform well and require reassessment. To optimize any company’s exposure, SaaS metrics for marketing are highly essential. They measure the performance of sales, marketing, and customer retention. SaaS companies believe in the entire life cycle of the customer, while traditional web-based companies focus on immediate sales. The overall goal of SaaS companies is to build long-lasting customer relationships since most revenue is generated through their recurring payments. SaaS marketing technology are SaaS marketers’ greatest asset if they take the time and effort to understand and implement them. There are essential and unimportant metrics. Knowing which metrics to pay attention to is a challenge. Once you get these metrics right, they will help you to detect your company’s strengths and weaknesses and help you understand whether they are working or not. There are more than fifteen metrics one can track but make you lose sight of what matters. In this article, we have identified the critical metrics every SaaS should track: Unique Visitors This metric measures the number of visitors your website or page sees in a specific time period. If someone visits your website four to five times in that given time period, it will be counted as one unique visitor. Recording this metric is crucial as it shows you what type of visitors your site receives and from what channels they arrive. When the number of unique visitors is high, it indicates to the SaaS marketers that their content resonates with the target customers. It is vital to note, however, which channels these unique visitors reach your website. These channels can be: Organic traffic Social media Paid ads SaaS marketers should, at this point, identify which channels are working and double down on those. Once you know these channels, you can allocate budgets and optimize these channels for better performance. Google Analytics is the best free tool to track unique visitors. The tool enables you to refine by dates and compare time periods and generate a report. Leads Leads is a broad term that can be broken down into two sub-categories: Sales Qualified Leads (SQL) and Marketing Qualified Leads (MQL). Defining SQL and MQL is important as they can be different for every business. So, let us break down the definitions for the two: MQL MQLs are those leads that have moved past the visitor phase in the customer lifecycle. They have taken steps to move ahead and become qualified to become potential customers. They have engaged with your website multiple times. For example, they have visited your website to check out prices, case studies or have downloaded your whitepapers more than two times. SQL SQLs actively engage with your site and are more qualified than MQLs. This lead is what you have deemed as the ideal sales candidate. They are way past the initial search stage, evaluating vendors, and are ready for a direct sales pitch. The most crucial distinction between the two is that your sales team has deemed them sales-worthy. After distinguishing between the two leads, you need to take the next appropriate steps. The best way to measure these leads is through closed-loop automation tools like HubSpot, Marketo, or Pardot. These automation tools will help you set up the criteria that automatically set up an individual as lead based on your website's SQL and MQL actions. Next, track the website traffic to ensure these unique visitors turn into potential leads. Churn The churn rate, in short, refers to the number of customers lost in a given time frame. It is the number of revenue SaaS customers who cancel their recurring revenue services. Since SaaS is a subscription-based service, losing customers directly correlates to losing money. The churn rate also indicates that your customers aren’t getting what they want from your service. Like most of your saas KPIs, you will be reporting on the churn rate every month. To calculate the churn rate, take the total number of customers you lost in the month you’re reporting on. Next, divide that by the number of customers you had at the beginning of the reporting month. Then, multiply that number by 100 to get the percentage. A churn is natural for any business. However, a high churn rate is an indicator that your business is in trouble. Therefore, it is an essential metric to track for your SaaS company. Customer Lifetime Value Customer lifetime value (CLV) measures how valuable a customer is to your business. It is the average amount of money your customers pay during their involvement with your SaaS company. You measure not only their value based on purchases but also the overall relationship. Keeping an existing client is more important than acquiring a new one which makes this metric important. Measuring CLV is a bit complicated than measuring other metrics. First, calculate the average customer lifetime by taking the number one divided by the customer churn rate. As an example, let’s say your monthly churn rate is 1%. Your average customer lifetime would be 1/0.01 = 100 months. Then take the average customer lifetime and multiply it by the average revenue per account (ARPA) over a given time period. If your company, for example, brought in $100,000 in revenue last month off of 100 customers, that would be $1,000 in revenue per account. Finally, this brings us to CLV. You’ll now need to multiply customer lifetime (100 months) by your ARPA ($1,000). That brings us to 100 x $1,000, or $100,000 CLV. CLV is crucial as it indicates whether or not there is a proper strategy in place for business growth. It also shows investors the value of your company. Customer Acquisition Cost Customer acquisition cost (CAC) tells you how much you should spend on acquiring a new customer. The two main factors that determine the CAC are: Lead generation costs Cost of converting that lead into a client The CAC predicts the resources needed to acquire new customers. It is vital to understand this metric if you want to grow your customer base and make a profit. To calculate your CAC for any given period, divide your marketing and sales spend over that time period by the number of customers gained during the same time. It might cost more to acquire a new customer, but what if that customer ends up spending more than most? That’s where the CLV to CAC ratio comes into play. CLV: CAC Ratio CLV: CAC ratio go hand in hand. Comparing the two will help you understand the impact of your business. The CLV: CAC ratio shows the lifetime value of your customers and the amount you spend to gain new ones in a single metric. The ultimate goal of your company should be to have a high CLV: CAC ratio. According to SaaS analytics, a healthy business should have a CLV three times greater than its CAC. Just divide your calculated CLV by CAC to get the ratio. Some top-performing companies even have a ratio of 5:1. SaaS companies use this number to measure the health of marketing programs to invest in campaigns that work well or divert the resources to those campaigns that work well. Conclusion Always remember to set healthy marketing KPIs. Reporting on these numbers is never enough. Ensure that everything you do in marketing ties up to all the goals you have set for your company. Goal-driven SaaS marketing strategies always pay off and empower you and your company to be successful. Frequently Asked Questions What are the 5 most important metrics for SaaS companies? The five most important metrics for SaaS companies are Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate. Why should we measure SaaS marketing metrics? Measuring marketing metrics are critically important because they help brands determine whether campaigns are successful, and provide insights to adjust future campaigns accordingly. They help marketers understand how their campaigns are driving towards their business goals, and inform decisions for optimizing their campaigns and marketing channels. How to measure the success of your SaaS marketing? The success of SaaS marketing can be measured by identifying the metrics that help them succeed. Some examples of those metrics are: Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the 5 most important metrics for SaaS companies?", "acceptedAnswer": { "@type": "Answer", "text": "The five most important metrics for SaaS companies are Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate." } },{ "@type": "Question", "name": "Why should we measure SaaS marketing metrics?", "acceptedAnswer": { "@type": "Answer", "text": "Measuring marketing metrics are critically important because they help brands determine whether campaigns are successful, and provide insights to adjust future campaigns accordingly. They help marketers understand how their campaigns are driving towards their business goals, and inform decisions for optimizing their campaigns and marketing channels." } },{ "@type": "Question", "name": "How to measure the success of your SaaS marketing?", "acceptedAnswer": { "@type": "Answer", "text": "The success of SaaS marketing can be measured by identifying the metrics that help them succeed. Some examples of those metrics are: Unique Visitors, Churn, Customer Lifetime Value, Customer Acquisition Cost, and Lead to Customer Conversion Rate." } }] }

Read More

Forward-thinking Business And The Implications Of Big Data

Article | July 22, 2021

Big data is a modern phenomenon transforming businesses of today. Organisations hold vast swathes of data, from historic and current orders to detailed insights about supply chain operations. This information, combined with external data such as market intelligence and even weather patterns, can provide businesses with a foundation on which to base their planning and decision-making. Business intelligence and analytical solutions pull valuable insights from huge datasets. From workforce optimisation to cost management, access to big data and the tools that manage and evaluate it allows firms to streamline key parts of their business. Adopters of modern solutions are seeing vast improvements in all areas of the company.

Read More

Is Augmented Analytics the Future of Big Data Analytics?

Article | July 22, 2021

We currently live in the age of data. It’s not just any kind of data, but big data. The current data sets have become huge, complicated, and quick, making it difficult for traditional business intelligence (BI) solutions to handle. These dated BI solutions are either unable to get the data, deal with the data, or understand the data. It is vital to handle the data aptly since data is everywhere and is being produced constantly. Your organization needs to discover any hidden insights in your datasets. Going through all the data will be doable with the right tools like machine learning (ML) and augmented analytics. According to Gartner, augmented analytics is the future of data analytics and defines it as: “Augmented analytics uses machine learning/artificial intelligence (ML/AI) techniques to automate data preparation, insight discovery, and sharing. It also automates data science and ML model development, management, and deployment.” Augmented analytics is different from BI tools because ML technologies work behind the scenes continuously to learn and enhance results. Augmented analytics facilitates this process faster to derive insights from large amounts of structured and unstructured data to gain ML-based recommendations. In addition, it helps to find patterns in the data that usually go unnoticed, removes human bias, and allows predictive capabilities to inform an organization of what to do next. Artificial intelligence has brought about an augmented analytics trend, and there has been a significant increase in the demand for augmented analytics. Benefits of Augmented Analytics Organizations now understand the benefits of augmented analytics which has led them to adopt it to deal with the increasing volume of structured and unstructured data. Oracle identified top four reasons organizations are opting for augmented analytics: Data Democratization Augmented data science availability to everyone has become a possibility thanks to augmented analytics. Augmented analytics solutions come prebuilt with models and algorithms, so data scientists are not needed to do this work. In addition, these augmented analytics models have user-friendly interfaces, making it easier for business users and executives to use them. Quicker Decision-making You will receive suggestions and recommendations through augmented analytics about which datasets to incorporate in analyses, alert users with dataset upgrades, and recommend new datasets when the results are not what the users expect. With just one click, augmented analytics provides precise forecasts and predictions on historical data. Programmed Recommendations Natural language processing (NLP) is featured on the augmented analytics platforms enabling non-technical users to question the source data easily. Interpreting the complex data into text with intelligent recommendations is automated by natural language generation (NLG), thus speeding up the analytic insights. Anyone using the tools can find out hidden patterns and predict trends to optimize the time it takes to go from data to insights to decisions using automated recommendations for data improvement and visualization. Non-expert users can use NLP technology to make sense of large amounts of data. Users can ask doubts about data using typical business terms. The software will find and question the correct data, making the results easy to digest using visualization tools or natural language output. Grow into a Data-driven Company It is more significant to understand data and business while organizations are rapidly adjusting to changes. Analytics has become more critical to doing everything from understanding sales trends, to segment customers, based on their online behaviors, and predicting how much inventory to hold to strategizing marketing campaigns. Analytics is what makes data a valuable asset. Essential Capabilities of Augmented Analytics Augmented analytics reduces the repetitive processes data analysts need to do every time they work with new datasets. It helps to decrease the time it takes to clean data through the ETL process. Augmented analytics allows more time to think about the data implications, discover patterns, auto-generated code, create visualizations, and propose recommendations from the insights it derives. Augmented analytics considers intents and behaviors and turns them into contextual insights. It presents new directions to look at data and identify patterns and insights companies would have otherwise missed out on completely- thus altering the way analytics is used. The ability to highlight the most relevant hidden insights is a powerful capability. Augmented analytics, for example, can help users manage the context at the explanatory process stage. It understands the values of data that are associated with or unrelated to that context, which results in powerful and relevant suggestions that are context-aware. Modern self-service BI tools have a friendly user interface that enables business users with low to no technical skills to derive insights from data in real-time. In addition, these tools can easily handle large datasets from various sources in a quickly and competently. The insights from augmented analytics tools can tell you what, why, and how something happened. In addition, it can reveal important insights, recommendations, and relationships between data points in real-time and present it to the user in the form of reports in conversational language. Users can have data queries to get insights through the augmented analytics tools. For example, business users can ask, “How was the company’s performance last year?” or “What was the most profitable quarter of the year?” The systems provide in-depth explanations and recommendations around data insights, clearly understanding the “what” and the “why” of the data. It enhances efficiency, decision-making, and collaboration between users and encourages data literacy and data democracy throughout an organization. Augmented Analytics: What’s Next? Augmented analytics is going to change the way people understand and examine data. It has become a necessity for businesses to survive. It will simplify and speed up the augmented data preparation, cleansing, and standardization of data, thus assist businesses to focus all their efforts on data analysis. BI and analytics will become an immersive environment with integrations allowing users to interact with their data. New insights and data will be easier to access through various devices and interfaces like mobile phones, virtual assistants, or chatbots. In addition, it will help decision-making by notifying the users of alerts that need immediate attention. This will help businesses to stay updated about any changes happening in real-time. Frequently Asked Questions What are the benefits of augmented analytics? Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs. How important is augmented analytics? Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors. What are the examples of augmented analytics? Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What are the benefits of augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics helps companies become more agile, gain access to analytics, helps users make better, faster, and data-driven decisions, and reduces costs." } },{ "@type": "Question", "name": "How important is augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics build efficiency into the data analysis process, equips businesses and people with tools that can answer data-based questions within seconds, and assist companies in getting ahead of their competitors." } },{ "@type": "Question", "name": "What are the examples of augmented analytics?", "acceptedAnswer": { "@type": "Answer", "text": "Augmented analytics can help retain existing customers, capitalize on customer needs, drive revenue through optimized pricing, and optimize operations in the healthcare sector for better patient outcomes. These are some of the examples of the use of augmented analytics." } }] }

Read More

Artificial Intelligence and Machine Learning on Agricultural Drones Market investigated in the latest research

Article | July 22, 2021

The agricultural drones market is projected to grow from $1.5 billion in 2018 to $6.2 billion in 2024, experiencing a 25.0% CAGR during 2019–2024 (forecast period). Crop spraying was the largest category in 2018, based on application, owing to the rising prevalence of fungal plant diseases caused by the Verticillium and Rhizoctonia fungi, which are spread by bollworm and flat armyworm.As these diseases destroy the yield, the agrarian community is deploying drones, also called unmanned aerial vehicles (UAV), to kill the pathogen.The rising adoption of such platforms for crop spraying is one of the key agricultural drones market trends. With UAVs, farmers can track their crops in distant locations in real time.Further, such vehicles ensure efficiency, by spraying only the required amount of liquid, which also checks wastage. For the purpose, multi-rotor UAVs are the most preferred choice, as they can hover over the spray zone.Currently, North America witnesses the heaviest utilization of drones for spraying insecticides and pesticides.The major driver for the agricultural drones market is the focus of farmers on enhancing the yield. Images to asses soil and field quality, crop growth and health, and hydric-stress areas are provided on a real-time basis by UAVs.

Read More

Spotlight

Pythian

Pythian is a global IT services company that specializes in designing, implementing, and managing systems that directly contribute to revenue and business success. We help companies adopt disruptive technologies to advance innovation and increase agility. Our highly skilled technical teams work as an integrated extension of our clients’ organizations to deliver continuous transformation and uninterrupted operational excellence using our expertise in cloud, DevOps, big data infrastructure, advanced analytics, infrastructure management, and databases…

Events