We all know that data is a company’s most valuable asset. But with an estimated 2.5 quintillion bytes of data generated each day, it’s a tough ask for any organisation to take advantage of it.

This is particularly apt in the BFSI (Banking, Financial Services, Insurance) sector.

Globally, the banking, finance and insurance industries handle millions of transactions, interactions and communications each day. It’s hard enough to keep up with what’s coming in, let alone look back over historic records to monitor emerging trends.

Yet, without the ability to process the vast amounts of data BFSI firms collect, there is literally a goldmine of information and insights going to waste.

Resource-heavy and time-consuming approaches to data capture, classification, validation and processing are no longer fit for purpose.

Luckily, machine learning (ML) heralds a new era for data-rich companies, like those in the BFSI sector.

What is machine learning?

Machine learning (ML) describes sophisticated computer systems that learn and adapt through the use of algorithms and statistical models. ML makes it possible to analyse large amounts of data to draw inferences, make connections and highlight trends.

Examples of ML use cases include chatbots. If you’ve ever clicked on the bottom right corner of a website, you will have encountered an automated communication system, programmed to respond to and resolve queries. This is just one example of ML in everyday life.

Other examples include:

• Image recognition (automatic tagging on social media)
• Speech recognition (voice search using smart home devices)
• Statistical arbitrage (automated trading via a Robo-advisor)

For BFSI firms, ML presents multiple opportunities to get a competitive edge. From improving the customer experience with personalisation to streamlining manual processes, ML is set to revolutionise how organisations in the finance sector do business.

12 benefits of machine learning

The ability to quickly process vast amounts of information delivers a host of benefits to organisations - not only to those in the BFSI sector.

1. Less human error
2. Saves time
3. Cost effective
4. Better analytics
5. Faster processing
6. Eliminates manual workload
7. Removes bias
8. Improves personalisation
9. Accelerates decision making
10. Strengthens fraud detection
11. Enhances risk management
12. Streamlines customer service

Far from eliminating humans from BFSI processes, ML allows your people to be redirected to other parts of the business. Instead of being underutilised in a repetitive role, they can add value by taking on higher-level tasks - such as fraud detection, risk management and data analysis.

3 ways to harness machine learning in the BFSI sector

Automation

Machine learning has the power to automate a raft of manual processes in banking and insurance. Bottlenecks occur when critical decisions need to be made by carefully assessing documents and evidence provided by customers. This is common with loan approvals, claims processing, credit applications and other high-risk processes.

Harnessing sophisticated algorithms, machine learning automates repetitive labour-intensive processes, such as data capture, document verification and identification checks. This speeds up processes without compromising data accuracy. Any errors or anomalies are automatically diverted for human intervention and investigation, ensuring end-to-end integrity and control.

Classification

Accurate document classification is critical for firms in the banking, finance and insurance sectors. Document classification determines subsequent action and escalation - incorrect classification leads to reverse workflow. Manual classification is no longer fit for purpose. Aside from the risk of human error, it also slows down the entire assessment and validation process.

One of the big advantages of ML is its ability to automatically index and classify structured and unstructured documents. This feeds documents into different workflows for action, assisting in the prioritisation of cases. Not only does this speed up the classification process, but it also frees staff for higher-level work, such as fraud detection, risk assessment and deeper analysis.

Validation

Data is invaluable - as long as it’s accurate. That’s why data validation is another source of pain for many BFSI firms. Traditional approaches are labour-intensive and time-consuming, requiring manual validation checks against multiple databases. With data holdings increasing exponentially, the old way is no longer an efficient or effective means of validation.

Machine learning eliminates human error while reducing the risk of fraud and unlawful activity. Advanced algorithms almost instantly complete validation checks that ordinarily take hours, if not days. This not only delivers a better experience for your customers. It also makes it easier for your firm to meet relevant regulatory and compliance requirements.

ML use cases for the banking, finance and insurance industries

Banking

• Credit underwriting - data point analysis quickly and accurately assesses borrower risk
• Payments - optimise payment routing to reduce transaction costs for merchants
• Fraud prevention - use detailed algorithms to detect potential threats in real time
• Anomaly detection - harness learning models to detect subtle signs of money laundering
• Hyper personalisation - analyse customer behaviour to offer targeted services
• Enhanced cybersecurity - leverage continuous monitoring to address security risks
• Risk management - benefit from deep data analysis to identify emerging risks
• Document processing - automatically classify, validate and organise documents
• Customer retention - utilise ML insights to identify new opportunities to serve clients.

Financial Services

• Loan pre-approvals - streamline classification and verification for faster approvals
• Financial forecasting - analyse historic data to predict future trends and risks
• Fraud detection - assess disparate data points to identify potential fraud
• Customer support - leverage chatbots to improve the customer experience
• Portfolio management - harness algorithms to automate and optimise investments
• Personalisation - tailor services and offers specifically to the needs of customers
• Task automation - automate manual processes to reduce errors and save costs
• Credit scoring - quickly understand the financial position of credit applicants
• Risk management - understand all relevant factors when making decisions.

Insurance

• Customer retention - identify policies likely to lapse and take remedial action
• Product recommendations - proactively help customers choose the right cover
• Assessor assistant - provide assessors with information for faster assessment
• Property analysis - use images and property details to suggest better coverage
• Fraud detection - assess disparate data points to identify potential fraud
• Personalised offers - target customers with offers relevant to their life stage
• Experience studies - analyse claims activity to assess risk and update assumptions
• Process automation - streamline underwriting, risk assessment and onboarding
• Risk assessment - utilise simulations to better assess risk and exposure.

Take advantage of ML with the help of Umlaut

Umlaut are leaders in digital transformation for finance and insurance firms. Our BFSI solutions leverage artificial intelligence, automation and machine learning to unlock a range of time and cost savings for your business.

Using our automation platform, we can offer a full-suite of intelligent document processing and automation solutions. Harnessing the power of AI, ML and deep learning, our Automation platform connects the dots for your BFSI firm, giving you a competitive edge.