Dorota Jasińska
Marcin Dobosz
Businesses in the financial services industry operate in a highly volatile environment. Risks include inadequate credit scoring, fraud attempts, operational failures, and compliance issues.
Any of these risks can cause significant financial losses and damage institutions’ reputation. The good news is that implementing predictive analytics in banking can effectively mitigate them. By offering reliable forecasts, this technology allows bank executives to reduce uncertainty across various operations and make more informed decisions with more confidence.
Given its benefits, It’s no surprise that investment in analytics technology is on the rise. In 2023, the global market for predictive analytics in banking was valued at just over $3 billion. It is expected to reach more than $16 billion by 2032, with a healthy CAGR of 20.6%.
Neontri has been at the forefront of developing and implementing sophisticated predictive analytics solutions for banks and fintech companies. In this article, we’ll explain the scope and role of predictive analytics, explore its key use cases, and show how it can help banks and financial institutions stay relevant, efficient, and competitive in a tough market.
Key takeaways:
- Financial organizations use predictive analytics more than businesses in other sectors due to high industry risks, the availability of large datasets, and rising competition.
- Predictive analytics helps banks spot fraudulent transactions, set accurate credit scores, mitigate risk, improve products, and make better decisions.
- When implementing predictive analytics, it’s important to focus on legal compliance, data quality, and system integration.
The role of predictive analytics in the banking industry
Predictive analytics is a forecasting method that predicts future outcomes or trends using artificial intelligence (AI) technologies and statistical algorithms. While businesses in all sectors use analytics to improve operational efficiency, BFSI organizations hold the largest market share for three main reasons:
- BFSI businesses operate in a high-risk environment. The pressure to reduce risk drives the adoption of new technologies, including advanced analytics.
- They also have access to vast amounts of data, including customer transactions and credit histories. This is ideal for predictive analytics, which needs large datasets to generate valuable insights.
- Increased competition from fintech companies has disrupted the traditional financial services market, forcing established players to focus more on customer retention and innovate rapidly.
In short, predictive analytics helps banks and financial institutions stay relevant, innovative, and competitive while effectively managing risk. Let’s explore its applications in more detail.
Predictive analytics use cases in banking
Predictive analytics can make a difference in a huge number of banking tasks and processes. Here’s where it can bring the biggest benefits.
Fraud detection and prevention
In 2022, losses from bank transfer and payment fraud reached a staggering $1.59 billion in the US alone. So, reliable fraud detection is more crucial than ever before. Predictive analytics enables banks to effectively detect fraudulent activities by spotting suspicious transactions. How does it work?
First, predictive models powered by sophisticated machine learning (ML) algorithms assign each transaction a risk score based on factors including location, time, amount, and transaction history. Any transaction that differs strikingly from the normal customer patterns of spending money qualifies as suspicious and can be automatically declined or sent for further review.
Second, predictive analytics risk scores are more reliable than traditional algorithms. This reduces the chances of a legitimate transaction being wrongly flagged as suspicious, which helps banks strike the right balance between fraud prevention and ensuring a positive customer experience.
Credit scoring
Assessing credit risks is another essential application of predictive analytics in banking. For instance, the industry standard FICO uses advanced analytics to evaluate borrowers’ creditworthiness based on five factors:
- their payment history
- credit utilization
- credit history length
- types of credit
- and recent credit inquiries.
A higher FICO score often translates into a higher likelihood of loan approval, as it indicates a lower risk of default.
However, advanced predictive modeling can even assess credit risk for borrowers without a credit history. By leveraging transaction history and other data from thousands of other borrowers, it can deliver accurate forecasts. This technology has contributed to the success of companies like ZestFinance and Upstart.
Risk management
Beyond credit scoring and fraud detection, predictive analytics can forecast future events that pose potential risks. For example, it can help you anticipate:
- Macroeconomic shifts, such as changes in interest rates, inflation, and currency exchange rates
- Future liquidity needs based on cash flow patterns, market conditions, and customer behavior
- Operational accidents like system failures, predicted from patterns in operational data
- Non-compliance risk areas before you incur penalties or legal issues
These insights help banks take a more proactive approach to mitigate risks, so they can protect their assets and minimize future losses. It’s a compelling advantage: 80% of respondents from the banking sector in PwC’s 2022 Global Risk Survey plan to increase their investment in data analytics to enhance risk management, with 41% expecting moderate increases and 39% anticipating significant ones.
Customer experience and personalized services
Enhancing product offerings and services is another way predictive analytics can help banks stay on top of their game. By anticipating customer needs, financial institutions can provide exactly what customers want when they want it.
The table below summarizes how implementing predictive analytics can support specific sales and marketing goals.
Customer service goals and predictive analytics in banking
Goal | How predictive analytics can help |
Personalization of customer experience | Analyze customer behavior, transaction history, and life events to suggest relevant products—for example, offering home insurance to a couple who just purchased their first home. |
Improved marketing efficiency | Segment customers and suggest how banking services can address the specific pain points of each group. |
Better customer acquisition | Map out the customer journey, identifying patterns to eliminate bottlenecks and streamline the onboarding process across channels. |
Better customer retention | Predict customer churn and approach at-risk clients proactively with a targeted offer. |
More profit from cross-selling | Identify cross-selling opportunities by analyzing customer behavior patterns. |
Increased customer lifetime value (LTV) | Forecast customer lifetime value, identify patterns shared by highest-LTV customers, and map the factors that contribute to LTV increase. |
Overall, predictive analytics helps you better understand your existing customers and prospects. Businesses can use these insights to offer them a memorable experience, resulting in higher customer satisfaction and retention.
Financial decision-making
Bank executives can rely on predictive analytics to identify the most promising business opportunities, including:
- High-return options for wealth management clients
- Best-performing assets for the bank’s own portfolio
- Products and services with the highest profitability potential
For example, predictive models can analyze historical data, interest rates, and economic indicators to forecast the potential performance of your investment assets. Citi Private Bank uses this advantage to find lucrative investment opportunities for its wealth management clients.
Using predictive analytics for banking services like these brings important benefits, but implementation comes with challenges. Let’s look at them next.
Key challenges in implementing predictive analysis
Reaping the benefits of predictive analytics takes careful planning and implementation since you’ll need to address legal, technical, and cybersecurity challenges. Here’s a brief overview of what to expect.
Legal compliance
When collecting and using data for predictive modeling, banks must comply with numerous regulations that impose strict rules, and non-compliance carries significant penalties.
For example, the EU’s General Data Protection Regulation (GDPR) mandates that customers must be able to obtain their data in a portable format, and they have the right to request the deletion of their personal data under certain conditions. Financial predictive models need to be built to comply with these principles.
In the US, the Gramm-Leach-Bliley Act (GLBA) requires banks to provide customers with clear privacy notices explaining how their financial data is collected and used. Meanwhile, the Fair Credit Reporting Act (FCRA) ensures that credit reporting is transparent and fair. Banks must adhere to the FCRA when using credit report data in predictive analytics for credit scoring.
Data quality
To achieve accurate forecasts, banks must collect extensive data, including transaction information, customer behavioral data from digital channels, market trends, and economic indicators.
However, data collection is only part of the task—predictive models also depend on data accuracy, completeness, and consistency. Poor data quality can lead to compromised predictions, resulting in misguided decisions.
The solution lies in establishing robust data governance frameworks. Before implementing predictive analytics, banks should invest in effective data management practices to eliminate data silos and ensure data is consistent and regularly updated.
This task, in turn, requires effective system integration, which creates another challenge.
System integration
Legacy systems and incompatible data formats are common in banking. Integrating them to create a cohesive data environment necessary for effective predictive modeling might be a complex and lengthy process. In many cases, legacy system modernization—upgrading or replacing software with modern solutions that meet current data analytics needs—must be completed before any integration efforts.
You may also want to connect your systems to external data to access market trends, social media sentiment, economic indicators, and other information required for predictive analytics. Integrating these data sources adds another layer of complexity to implementation initiatives.
While all these challenges can seem daunting, partnering with a reliable provider can make a significant difference. An experienced partner can offer the expertise necessary to navigate the complexity of predictive analytics implementation.
Predictive analytics trends and emerging opportunities
Predictive analytics for banking and financial services is advancing rapidly, creating new opportunities in the sector. Here are some of the biggest trends:
- Hyper-personalized offerings. With predictive analytics, banks can go beyond customer segmentation and tailor offerings to individual contexts and preferences. For instance, a young lawyer with a stable income and minimal debt might receive an offer for a low-interest loan to fund a home purchase.
- Real-time analytics. This involves the immediate processing and analysis of data as it’s generated. With real-time analytics, bank managers can make faster and more efficient decisions, particularly in areas such as fraud detection and risk assessment.
- Cross-industry applications. Banks are increasingly collaborating with businesses in different sectors for improved predictive analytics insights. For example, a bank might partner with a retail company to analyze customer behaviors and preferences and then use this information to personalize credit card offers.
These are just a few of the many market trends in predictive analytics you can follow to maintain a competitive advantage, reduce risks, and improve operational efficiency. However, as mentioned, the sector is dynamic, making it essential to stay updated on advancements.
Build predictive analytics capabilities with Neontri
Shifting to predictive models means dealing with legal compliance, data quality, and system integration challenges, which is where a reliable partner can make the difference. With nearly 15 years of experience in creating custom software solutions for banking, fintech, and retail, Neontri has developed a deep understanding of these challenges and how to overcome them effectively.
Our expertise in the financial sector enables us to guide our clients through the complexities of implementing predictive analytics, helping them avoid common pitfalls and achieve better business outcomes.
Final thoughts
Predictive analytics offers attractive benefits to businesses in the financial industry, particularly banks. Implementing this technology helps you mitigate critical risks such as inaccurate credit scoring and fraud, enhance decision-making through data-driven insights, and cultivate stronger customer relationships through better products and services.
Contact us today and take the first step toward data-driven operational excellence!
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