Transforming Risk Management with AI-Powered Analytics


The acceleration of the digital world are pushing banks to better understand, predict and protect against traditional risks as well as emerging ones such as global pandemics and climate change. Artificial intelligence (AI) tools can help them forecast activities, understand trends and analyze possibilities in near-real time to safeguard their business and deliver stronger results to customers.

To manage risks in real time and make intelligent decisions, financial institutions are prioritizing advanced analytics by using AI to extract deeper insights. The most advanced banks are starting to utilize neural nets and deep learning, which can ingest millions of data points in milliseconds to detect problems.

With advanced and predictive analytical capabilities, AI can augment human-led risk management activities to drive better outcomes much faster. It is estimated that through better decision-making and improved risk management, AI could generate more than $250 billion in the banking industry.

Here are three ways on how AI and ML can help financial institutions identify risk in an effective and timely manner, make more informed credit decisions and improve all aspects of regulatory compliance.

  • Fraud Detection & Analytics

Machine learning algorithms can spot discrepancies, inconsistencies, and unusual patterns faster and more accurately as they’re not restrained to a limited number of variables. ML can also help lenders cross-reference applications and uncover additional relevant information. And the more datasets models review, the “smarter” their predictive capabilities and risk profiles become.

The idea behind ML-driven fraud analytics is that fraudulent transactions have telltale signs that algorithms can uncover much more effectively than rule-based monitoring systems. By processing customer, transactional and even geospatial data, they can even spot patterns that seem unrelated and simply go unnoticed by human data analytics.

  • Credit Risk Prediction

Credit risk is based on the potential loss suffered when borrowers or counterparties fail to make payments on debts. Here, banks are using ML and natural language processing (NLP) technologies to conduct more expansive and thorough probability-of-default analysis as well as enhance their detection of early warning signs.

By analyzing a vast amount of financial and non-financial data, trained machine learning algorithms can model credit risk and predict default with a much higher degree of accuracy than traditional methods.

  • Regulatory Compliance

Regulatory compliance is a rigorous and complex process for banks. They constantly face the risk of legal sanctions, financial loss, or negative impacts on their reputations because of failure to comply with laws and standards. To mitigate this risk, many banks are looking to confidential computing technologies that help streamline compliance while dramatically improving the security of sensitive workloads and data.

ML solutions help reduce and effectively eliminate the number of false-positive alerts in compliance systems. By learning from the compliance officers’ data, machine learning programs can increase efficiency and accuracy, streamline operations, and reduce costs by only surfacing alarms when the detection system isn’t sure and human expertise is needed.

SYNERGi: The AI-Powered, Integrated Risk Management

With the SYNERGi AI & Analytics solution, banks can leverage on a suite of powerful tools to provide real time and predictive analytics to explore evolving customer needs, predict business trends, measure performances from all perspectives and monitor expected and unexpected risks in order to carve out precise and effective business strategies.

The robust solution empowers FIs with the ability to blend, explore and analyze data from a range of structured and unstructured sources and derive powerful new insights. The applications also provide customizable, visually engaging dashboards which can be accessed from anywhere, helping deliver an appealing self-service customer experience.

Banks and financial service providers are perpetually redefining their products to meet the ever-evolving customer expectations. This is where INFOPRO helps them make a definitive value addition by bringing predictive analytics capabilities to the table. In other words, SYNERGi AI-powered predictive analytics will enable banks and financial institutions to regularly revisit and rediscover their offerings, create suitable value propositions, and elevate customer experience.

Predictive Analytics applies their data to support the organization to make an official decision. It is caused by calculating data relevant conclusions of past, present, and future. It empowers the organization to concentrate on discovering their business issues proactively by addressing them in real-time to find the right customers.

For more information, visit or click here to schedule a demo with our business analytics expert.

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