News!: Opus Technologies Launches FinGeniusAI Solutions – An Open Innovation Platform for Building Future-Ready Solutions.. Know More
News!: Opus Technologies Launches FinGeniusAI Solutions – An Open Innovation Platform for Building Future-Ready Solutions.. Know More


Fostering Open Innovation in AI to Enable Seamless Banking and Frictionless Payments

Introducing FinGeniusAI

To drive the adoption of AI in the banking and payments industry, Opus has launched FinGeniusAI. It's a platform that invites key industry players - clients, partners and other financial institutions - to build future-ready solutions with Opus. It's an initiative to harness the collective expertise to shape the future of banking and payments.

FinGeniusAI has three niche use cases to showcase the power of our platform. We strive to add many more in the near future, including Generative AI and large-language models (LLMs) based applications.

Sign up for a PoC with Opus to achieve quantifiable results with no/minimal investment and gain a deeper understanding of AI-based solutions.

Use Case: Intelligent Payment Routing

Maintaining high acceptance rates of your transactions can be challenging in today’s ever-shifting payments landscape. Optimize your payments performance with smart routing to curtail costs and prevent delays in transactions. Power your payment engine and reduce cart abandonments to achieve a high level of customer satisfaction.

Does This Sound Familiar?

Banks and Credit Unions: Mostly use payment engines driven by rule-based logic for determining payment routing. These rules are difficult to maintain and need careful regression testing, which is time consuming. Breaks in existing flows are commonplace, resulting in high overhead costs.

Merchants: Loss of revenue from cart abandonments due to limited payment methods and failed authorizations as well as pressure on margins from higher interchange fees.

AI to the Rescue

Machine learning can replace rule-based configurations to suggest the most efficient payment routing for retail and commercial transactions through various payment methods. This can be applied for account-to-account payments routed through payment methods like RTP, RTGS, ACH, and SWIFT. It can also be applied to different payment processors, acquirers, PSPs, and alternate payment methods.

Some of the parameters that can be configured to optimize payment routing are – the least cost of the transaction, the failure rate of the payment processor, transaction amount, transaction capture device, and chargeback history. In case of a retry, the system can be configured to consider additional parameters, such as previous error type, merchant type and transaction type.

Business Benefits

  • Simplifies payment routing and lowers errors and false declines
  • Low implementation time and cost after the model is trained
  • Low maintenance time and cost
  • Reduced compensation cost for delayed payments for banks and credit unions
  • Higher conversion rates for merchants

Use Case: IntelliSearch AI – Document Search and Analysis through GenAI

With the vast amount of unstructured data stored by banks and financial institutions, finding relevant information at the right time is a daunting task. Existing search systems rely on keyword-based queries. The output is not user-centric and lacks context and insights. This leads to significant operational inefficiencies and can result in poor customer experience.

Does This Sound Familiar?

Traditionally, banks and financial institutions rely on manual search or keyword-based search software to find relevant data. Keyword-based search engines can significantly slow down a bank’s operations. Employees waste time sifting through irrelevant search results due to the lack of context and precision with keywords. This can lead to delayed decisions in loan processing and customer service, as critical information retrieval is incomplete or inaccurate. Furthermore, misinterpreting search results or missing crucial details can cause errors that require rework.

AI to the Rescue

Powered by GenAI and LLMs, IntelliSearch AI can empower your financial organization with insights from business-critical data. Unlike traditional search engines, IntelliSearch AI not only finds relevant information faster and with accuracy, but it also delivers the information in a clear and organized format. So, instead of just identifying the document that contains relevant information, IntelliSearch AI can interpret essential data with precision and present an easy-to-consume summary or key insights based on the context shared by you.

It’s a powerful GenAI tool that can be configured to search for information from a range of sources, such as corporate knowledgebase, resource libraries, Known Error Database (KEDB for support function), frequently asked questions, developer’s portal, or onboarding portal. IntelliSearch AI platform can handle unstructured data, complex queries and increasing document volume without compromising speed, accuracy and quality of results.

Business Benefits

  • Achieve higher operational efficiency
  • Reduce operational cost
  • Faster decision making
  • Enhanced customer experience
  • Reduce the risk of manual errors

Use Case: Cash Flow Forecasting

Cash flow forecasting is critical for making smart business decisions. Does this justify investing hours in gathering and aggregating transaction data? How do you prevent the process from being riddled with errors? Turbocharge your cash forecasts with machine learning and artificial intelligence.

Does This Sound Familiar?

Liquidity surplus and shortage are both bad news for businesses and financial institutions. One suggests lost opportunities, while the other comes with the elevated cost of securing additional funds. Missed opportunities, delayed payments, regulatory penalties, and severed correspondent banking relations, are on the top of the minds for treasurers.

AI to the Rescue

Deploying ML and AI techniques makes managing liquidity far more efficient. This replaces rule-based or manual processes with powerful adaptive learning in predictive analysis to determine cash flow requirements based on historic cash flow data, cyclical nature, market outlook, organization outlook, etc. It can be applied for managing corporate liquidity, funding for nostro accounts and settlement accounts.

Business Benefits

  • Avoid higher cost of securing last minute funds
  • Invest excess funds for higher returns
  • Less customer dissatisfaction due to delayed payments
  • Avoid regulator penalties
  • Maintain good correspondent banking relations

Use Case: Payment Enrichment with Auto-Repair

STP (straight through processing) means faster processing speeds, lower costs, fewer errors, and improved customer service. Despite these many benefits, six of every seven transactions are completed manually, due to incomplete implementation, low data integrity, non-standard processes, etc. Increase your STP rates with machine learning and artificial intelligence.

Does This Sound Familiar?

While Banks and Credit Unions continuously strive to improve their STP rates, payments consistently fall due to faults in the source of data. This results in either manual repairs, which are riddled with errors, or rule-based repairs, which translates to higher operational overheads. The longer the transaction remains in the repair queue, the greater is the dissatisfaction among customers.

AI to the Rescue

Machine learning techniques can auto-repair payments based on past fixes. This is because most payment faults are due to missing purpose information and insufficient or ill-formatted details of the originator and beneficiary banks.

FinGeniusAI  introduces a payment enrichment layer into the transaction processing flow. This innovative layer acts on the machine learning models trained on historical error patterns to proactively identify potential issues in new payments. The enrichment layer could be configured to automatically enrich specific fields based on a confidence score, or mark an issue for manual intervention enabling operators to make informed decisions. The enriched payment data is passed to the payment engine to ensure higher STP rates.

Business Benefits

  • Faster payments
  • Higher STP rates, resulting in lower costs and fewer errors
  • Lower operational overheads
  • More satisfied customers
  • Higher customer retention

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