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Use Cases: How to Implement Predictive Analytics in FinTech

“For every human, there are 2.5 million ants on Earth.” — that’s analytics. Predictive analytics can tell you how many ants per human we will have in 100 years. What can it do in Fintech?

Find out the benefits and challenges of predictive analysis in the Fintech industry, where you can apply it, and how to best implement it in your product or service.

P.S. What do you think: can the 2.5 million ants nature has in store for you outweigh you? Find out at the end of the article.

Predictive analysis and its methods explained

Predictive analytics uses statistics, machine learning algorithms, and other methods to spot patterns and make predictions for businesses and their customers.

To conduct this type of analysis, your teams need to collect and preprocess the data. Then you select suitable modeling techniques, train the models, and check their accuracy. Focusing on the specific needs and goals of your business, you can choose from various software tools and platforms. You can also use several methods from the ones below, each one alone or in combination, to achieve your goals with analytics.

Artificial intelligence (AI): AI techniques and technologies help analyze historical and current data to predict future events or trends. It employs statistical algorithms and machine learning models to spot patterns, links, and trends within the data. This process enables organizations to anticipate outcomes and inform their decisions with insights derived from relevant data.

Data mining: It’s the systematic extraction of meaningful patterns and insights from extensive datasets, utilizing techniques from statistics, machine learning, database management, and artificial intelligence for informed decision-making and predictive analysis.

Machine learning (ML): Machine learning, a subset of artificial intelligence, lets systems learn from data and enhance performance over time. In finance, it finds application in fraud detection, investment decision-making, and predicting market trends.

Modeling: In predictive analytics, modeling is the creation and utilization of mathematical or computational models to predict, classify, or extract insights from data. Using modeling, you can create algorithms capable of learning from historical data, enabling them to generalize patterns and make informed decisions about new, unseen data.

Statistics: It plays a vital role in extracting meaningful insights from data by providing a framework for making inferences and decisions through the collection, analysis, interpretation, presentation, and organization of data.

Mixed approach: Predictive analytic software can blend conventional statistical methods, machine learning, and innovative data mining techniques. For instance, in portfolio risk prediction, these advanced models go beyond user-specified predictors. Instead, they autonomously uncover predictors by analyzing the intricate interactions of multiple, nonlinear risk variables. This approach identifies specific combinations of risk factors that can help predict future outcomes.

Common misconceptions about predictive analytics

  1. Predictive analytics vs. Machine learning 
  2.  
  3. Predictive analytics uses past data to make informed guesses about future events. It involves looking at historical information using statistical techniques and machine learning to estimate or predict what might happen next based on what we've seen before. It's a way of making informed predictions by learning from the patterns in data. So, machine learning is a means that predictive analytics uses.
  4.  
  5. Predictive analytics vs. Prescriptive analytics

  6. Predictive analytics anticipates future events, while prescriptive analytics goes a step further, suggesting actions based on those predictions. For example, if predictive analytics shows a company's future revenue might stall, prescriptive analytics helps choose the best growth strategy. It models potential outcomes for different approaches, guiding decisions toward the most promising path, like selecting a product likely to boost profits.
  7.  
  8. Use-Case2

 

Why FinTech needs predictive analytics

There are multiple benefits to using predictive analytics, from improved risk management and better investment decision-making to enhanced fraud detection, more efficient operations, and overall competitive advantage. 

  • Predictive analytics supports effective risk assessment and management for FinTech companies.
  • Machine learning algorithms enable continuous adaptation to new fraud patterns, enhancing security measures.
  • In lending, predictive analytics plays a crucial role in credit scoring and underwriting processes. It analyzes credit history, income, and spending patterns for more accurate creditworthiness assessments.
  • By analyzing customer data, fintechs can offer tailored financial products, investment recommendations, and personalized user experiences, boosting customer satisfaction and loyalty.
  • Fintechs leverage predictive analytics to optimize operations, customer retention, and pricing models. This includes forecasting demand, efficient resource allocation, and automating routine processes for increased efficiency.

Use cases for predictive analysis in FinTech

In finance, this type of data analysis plays a crucial role in forecasting market trends, managing risks, and optimizing investment decisions. 

Credit

Credit scoring relies heavily on predictive analytics. When an individual or business seeks credit, the process involves utilizing data from the applicant's credit history and the credit records of borrowers with similar characteristics. This information is employed to forecast the likelihood that the applicant might encounter difficulties fulfilling obligations related to any extended credit.

Risk assessment and management

Predictive analytics lets financial professionals evaluate and handle risks related to investments, loans, and other financial products. By scrutinizing historical data and uncovering trends and correlations, these algorithms help pinpoint potential risks and opportunities and support decision-making concerning risk management in financial institutions.

Investment analysis and portfolio management

Businesses can assess investment performance and predict future returns by analyzing historical market data and identifying patterns and trends. This enables financial professionals to make informed strategic decisions regarding portfolio management.

Underwriting

Predictive analytics and data can heavily support the underwriting process for insurance companies. By assessing policy applicants and analyzing the risk pool of similar policyholders and past claim events, insurers can predict the likelihood of future payouts. Actuaries commonly employ predictive models that take into account various characteristics and compare them to data on past policyholders and claims for more accurate risk assessment.

Customer segmentation and targeting

Financial institutions leverage these processes to segment their customers, considering factors like income, credit history, and spending behavior. Predictive analytics makes this segmentation possible and enables a more profound insight into customer needs and preferences. Subsequently, institutions can create precise digital marketing campaigns and customize their products and services to cater to the specific requirements of distinct customer groups.

Budgeting and resource allocation

Leveraging predictive analytics allows financial institutions to look at future financial performance by scrutinizing historical data for trends and patterns. These algorithms aid in predicting upcoming revenue, expenses, and overall profitability. This foresight empowers financial institutions to make strategic decisions on resource allocation and future planning based on informed insights.

Fraud detection and prevention

Predictive analytics in financial services enables examining transactions, trends, and patterns. Any irregular activity is scrutinized for potential fraudulent behavior. This analysis may involve studying activity between bank accounts or identifying unusual patterns in transaction timing.

Use-Case1 (2)

Implementing predictive analytics in financial products and services

Choosing the right model

Below, you’ll find two groups of models that represent different perspectives on predictive analytics. They are not exactly subgroups of each other, but they can be related based on the context of their applications. Let's break down each group.

Predictive analytics models are designed to assess historical data, discover patterns, observe trends, and use that information to predict future trends.

There are three prominent predictive models widely utilized in the financial sector:

Classification model: The binary output of this model produces predictions by broadly assessing the subject. In banking, for example, it can forecast if the shares of a particular firm are likely to go up or down.

Outliers model: Tailored to detect significant deviations in a dataset, the outliers model is especially valuable for fraud detection. Say, there was an odd purchase from a customer's credit card in a town they don't frequent; as the action is unusual, the outlier model would mark it as potentially fraudulent.

Time series model: Focused on monitoring a specific variable over a certain time period, the time series model predicts how that variable will be influenced at another particular time frame. This model is commonly used in finance to forecast changes such as prices of securities or economic conditions, over time.

While these models serve distinct purposes, they can be applied in related domains. In trading, for instance, you can use time series models to predict stock prices (regression or forecasting), and an outliers model helps pinpoint potentially fraudulent trading activities.

Next come data science models used in predictive analytics.

Statistical models. These process numerical data and calculate future metrics using trends. They are less costly than the other two and are best used for predicting stable quantitative KPIs in finance.

Non-neural network (non-NN) machine learning models. Non-NNs process multi-dimensional data (needs to be structured) and forecast a wide range of variables (e.g., risk, revenue, expenses, etc.) by analyzing various factors for each variable. This makes them excellent for batch predictive analytics but also quite expensive.

Deep neural network (DNN) models. These leverage AI and require the most cost compared to the other two. The best use for DNN models is real-time predictive analytics, as they deal with vast amounts of financial data, both raw and structured. DNN models automatically identify changing factors for the necessary variables and provide precise predictions by recognizing and analyzing intricate non-linear dependencies. 

Statistical models are a broader category encompassing traditional statistical techniques. In contrast, non-neural network machine learning models and deep neural network models represent different paradigms within the broader field of machine learning.

While there can be some overlap and integration between these categories, especially with advancements like neural network-based time series models, they are generally distinct in terms of their underlying principles and methodologies. In practice, the choice of model depends on the nature of the data and the specific goals of the analysis. 

I explain more about AI-based models here.

Choosing the right architecture

When choosing architecture for a predictive analytics solution in FinTech, you need to consider several factors, including the nature of the data, the complexity of the analytics tasks, scalability requirements, and regulatory considerations. Let’s look at the FinTech architectures best suited for predictive analytics.

Microservices

In a microservices architecture, the application is divided into small, independent services. These can be developed, deployed, and scaled separately from each other. Each microservice is responsible for a specific business capability.

This type of software architecture is a good fit for FinTech applications handling diverse tasks, such as risk management, fraud detection, and customer segmentation, allowing each service to specialize in a specific area. Microservices will add scalability and agility to your solution, allowing for easier maintenance, updates, and fault isolation.

Event-driven architecture
Event-driven architecture processes events asynchronously, reacting to events and triggering appropriate responses. This is particularly useful for real-time analytics, improving processing, responsiveness to changing conditions, and scalability.

You can use event-driven architecture for real-time fraud detection, market monitoring, and personalized customer interactions.

Data lake architecture

A data lake is a centralized repository, where your business can store structured and unstructured data at any scale. It enables you to run analytics tools and machine learning algorithms on diverse datasets with centralized data storage and flexibility in handling diverse data types. Like the previous two architectures, this one is scalable.

For FinTech predictive analytics, a data lake can store historical transaction data, customer information, and external market data, providing a comprehensive source for analysis.

Serverless architecture

Serverless computing lets developers write code without managing the underlying infrastructure. In this type of architecture, functions are executed in response to events or triggers.

This architecture is cost-effective, ensuring automatic scalability and reduced operational overhead. It can be employed for specific functions within a FinTech analytics pipeline, such as data preprocessing or model inference.

Below, you can see an example of a mixed architecture: a combination of microservices architecture and elements of event-driven architecture.

Use-Case3

In this example, real-time and batch insurance data processing and analysis occur in distinct flows. A previously trained DNN autonomously generates precise forecasts for the essential variables. These predictions are stored and visualized, helping teams in operational and strategic planning. Real-time insights are promptly sent to relevant systems, triggering events like price notifications, fraud alerts, and dynamic premium adjustments.

Possible pitfalls and challenges and how to battle them

Predictive analytics may bring many opportunities, but it’s not without the hard part. Below you’ll find several of the most common predictive analytics pitfalls and how to overcome them.

Data quality. Incomplete, outdated, or faulty data can compromise predictive modeling, resulting in inaccurate predictions. So, make sure to check data quality when collecting information from diverse sources. Implement robust data governance practices to maintain structured and consistent data management, minimizing the risk of incorrect or misleading predictions and insights.

Cost. As I said, keeping the quality of your data high is a must. But that can be costly, requiring investments in data cleaning tools and processes. Things like building, maintaining, and scaling the necessary technology infrastructure; model development and testing; and employing skilled data scientists, analysts, and professionals with expertise in predictive analytics can bite quite a lot off your budget. If you’re thinking about finding a vendor and integrating their predictive analytics solution into your systems, it’s worth assessing your resources, too.

Privacy and security. Tons of personal data your analytics will rely on must be treated carefully and according to the relevant regulations and standards. As the amount of data used increases yearly, so do concerns about its privacy and the fairness of the processes. Make sure your teams develop strong data protection policies and procedures to safeguard customer information from irresponsible and unprotected usage, leading to fraud or identity theft.

Model overfitting. Overfitting occurs when a model learns not only the underlying patterns in the training data but also captures noise or random fluctuations. This sensitivity makes data too specific to the training set. In the context of FinTech, overfitting can lead to various problems, such as misleading results, poor generalization to new market conditions, and inaccurate risk assessment. All of that will likely lead to a negative impact on the customer’s portfolio, amounting to hefty losses in some cases.

To mitigate overfitting issues, you need to implement robust model validation and regularization techniques. Also, ensure the use of diverse datasets that represent the complexity of real-world scenarios. Remember: regular monitoring and updating of models are essential to maintain their accuracy and relevance in the domain where the context changes every second.

Lack of interpretability. Interoperability allows different systems, applications, or components to effectively exchange and use information in an orchestrated way. When systems work in chaos and don’t share crucial data, that leads to model bias and problems with establishing model governance, lack of transparency in communication with stakeholders, and non-compliance with regulations. All of the above eats at your business reputation and customers' and partners' trust.

Each of the challenges I mention above can sprout into new micro-challenges, growing big and severe. It’s crucial to understand your business context and the uniqueness of your product or service to provide more detailed recommendations. If you want to know the best way to implement predictive analytics in your business, ask us at INSART. We’re always up for helping fintechs grow and transform people’s financial lives for the better.



The answer: No, luckily or not, your weight is still bigger than that of 2.5 million ants. Collectively, they weigh approximately 5 kg.

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