by Navin Gupta, on Nov 24, 2022 8:07:02 PM
Estimated reading time: 4 mins
Key Takeaway from this blog- AI/ML-powered Risk Management solutions provide a competitive advantage to Banking and Financial Services by predicting potential future outcomes based on current data.
- They offer transformative solutions to BFS entities that were unimaginable in the past. Accurate forecasting and eDiscovery are some of the prominent use cases.
- They analyze vast amounts of data and abstract complexities to provide accurate and traceable outcomes for quick human resolution.
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Banking and Financial Services deal with customers’ money to create opportunities for them and expand their services. They sometimes deal physically with cash, but they handle data across digital channels. This heavy data usage must be correct and accurate to deliver timely services. They have to monitor risk avenues and eliminate them. This task requires skill and intensive human effort. However, getting timely results and tying this human effort-intensive skill to business is challenging. Banking and Financial Services drive the pulse of the national economy, and any slackness can derail the entire ecosystem. Therefore Risk Management in Banking and Financial Services has to be prompt and ongoing. Cloud Native Artificial Intelligence/Machine Learning (AI/ML) solutions help automate the Risk Management aspect of the business while handling hard-earned customer monies and raise timely flags to mitigate risk.
AI/ML as the game changer in Banking and Financial Services
Risk Management is vital in all businesses, predominantly Banking and Financial Services. Accurate. Timely data analysis is crucial for eliminating potential loss. AI/ML enables businesses to derive patterns and trends from data that are not visible to even the most dedicated and sophisticated human analysts.
AI/ML is a game changer for data analysis in Banking and Financial Services. It is used most effectively and efficiently in customer intelligence generation, risk management, credit decision management, credit risk management, customer service, customer experience management, etc. Most importantly, the data insights are traceable across the data stacks for human validation.
AI/ML gives the best results with huge amounts of data. Naturally, Cloud and AI/ML enable businesses to build models that quickly resolve the most complex business issues. After some initial training or supervised machine learning, the AI/ML models learn exponentially through self-learning and generate accurate outcomes with little to no human intervention. AI/ML enables businesses to adopt a transformative approach that was unimaginable until the past few years.
Risk Management in Banking and Financial Services – AI/ML Use Cases
Data-driven Risk Models powered by Cloud Native AI/ML solutions or Cloud Native Intelligent Automation enable Banking and Financial Services enterprises to leverage a transformative approach. The effort requires little to no human intervention and works 24x7 in the background with timely alerts for immediate action. Some of the most important AI/ML use cases in Banking and Financial Services are –- Credit Rating: It enables augmenting massive data related to institutional and retail entities and building credit rating scores that allow institutional investors to navigate their business transactions with these entities.
- Credit Risk Monitoring: It enables scrutinizing each transaction involving credit to customers or on behalf of customers. It allows for re-assessing the credit risk profile of the party on an ongoing basis.
- Fraud Detection & Risk Monitoring: It enables Banking and Financial Services to generate non-linear forecasts at an early stage based on customers’ to and fro transactions involving different parties. Fraud Risk Governance is vital for enterprises on dynamic business landscapes.
- Contract Analytics: It helps AI/ML-driven analysis of contracts and agreements to discover performance insights vis-à-vis banking and business issues as well as state regulations. It enables customer relationship engagements on an ongoing basis.
- Probabilistic Analysis: It helps assign a value to uncertainties and risks. Instead of analyzing a best case and the worst case for Risk Assessment and Management, it is better to bring out all the intermittent cases based on probabilistic distribution and analysis.
- Legal document e-Discovery: It helps classify documents, extract data, analyze text, create summaries, and assign risk scores for each clause and the comprehensive legal document. It helps mitigate data management issues resulting from high volumes of lengthy legal documents.
Access more Intelligent Automation use cases >>
Benefits of AI/ML in Banking and Financial Services related Risk Management
Risk Management using AI/ML solutions benefits Banking and Financial Services in many ways –
- Accurate forecasting: The forecasts are highly accurate even with little to no human intervention. It eliminates the requirement of having skilled human personnel to monitor the millions of transaction gates at all times and manage only by exception.
- Scalable: The AI/ML-powered Risk Management solutions engage Cloud Native environments and hence are highly scalable to suit evolving business requirements. The solutions are highly customizable and containerized with no Cloud vendor lock-in.
- User-friendly: The user interfaces are intuitive and user-friendly, allowing business users to operate them efficiently.
- Faster decision-making: Risk Management solutions help Banking and Financial Services entities to make faster decisions on dynamic business landscapes. The unified data displays alerts on the user interface. They are entirely traceable to the data points.
- Competitive differentiation: Risk Management solutions are competitive differentiators for Banking and Financial Services entities. They help to evaluate the potential outcomes that are beneficial for the business. They help build defense strategies and alliances.
- Eliminate human-intensive calculation: AI/ML-powered Risk Management solutions automate the most complicated processes involving humongous data across different touch points. They require little to no human involvement and offer faster calculations for decision-making.
Summary
AI/ML-powered Risk Management Solutions offer a competitive advantage to Banking and Financial Services by automating human-intensive tedious calculations involving voluminous data. The solutions are scalable and offer highly accurate outcomes in the form of business alerts for immediate human resolutions. Risk Management solutions help discover patterns that are not easily visible to even experienced analysts. The solutions abstract complicated calculations and provide an easy and intuitive user interface.
