BFSI IT Summit

Operational Resilience in Financial Services

How AI is Transforming in Banking and Financial Industry.

Over the past decade, we have seen substantial growth with respect to digital transformation in banking and financial services. However, the rise of fintech has increased regulatory pressure on banks and financial institutions to closely comply with regulations while also enhancing the customer experience and operational efficiency.

The introduction of Artificial Intelligence (AI) in banking could be the key to enhancing operational efficiency and meeting the demands of banking and financial services. From analyzing customer behavior to improving back-office operations, AI in banking is poised to drive incalculable value more than ever before.

Here are five ways through which banking and financial services can leverage AI

1.AI in regulatory compliance

    Regulatory compliance in banking has always been a human extensive work, with no clear ROI. AI-based software assists institutions in facilitating regulatory compliance more efficiently and effectively than current capabilities.

    AI can help compliance officers supplement their skills while also helping them to scale their operations beyond what is achievable manually. Machine learning (ML) paired with natural language processing (NLP) can help comprehend data inputs such as e-mails, spoken words, instant messaging, and documents, reducing the burden on officers even further.

    2. AI for data quality assurance and macro prudential surveillance

    In the banking sector, the volume of data received is usually enormous and it becomes difficult for authorities to process the same using traditional methods.

    By automating macro prudential analysis and data quality assurance, AI and machine learning tools may help to improve macro prudential surveillance by automatically detecting potential mistakes and alerting the data source.

    3. AI for surveillance and fraud detection

    Artificial Intelligence (AI) can be used to spot complicated patterns and questionable transactions that require further examination. AI may be used to analyze granular data from transactions, client profiles, and a range of unstructured data when combined with machine learning algorithms.

    It can also help institutions detect non-linear correlations between distinct traits and entities, as well as potentially intricate money-laundering behavior patterns.

    4. AI for knowing the customer

    One of the biggest challenges faced by banking and financial services in terms of both customer experience and regulator demands is the Know Your Customer (KYC) process.

    During remote KYC, AI can assist financial services organizations in performing identification and background checks. It can also help in determining whether the photos in identifying documents match each other.

    5. AI for systemic risk identification

    Authorities can use machine learning and natural language processing (NLP) methods to predict and anticipate market volatility, liquidity issues, financial stress, and even unemployment.

    AI may also be used to integrate and compare trading activity data with behavioral data to provide more accurate analysis. Even though many institutions are concerned about ROI, there are a considerable number of institutions that have adopted technological innovation and have experienced substantial returns.

    If your organization is looking to embrace these new technological advancements, join us at the BFSI IT Summit Africa, a one-day virtual conference to learn more about how other institutions are driving digital transformation and re-inventing digital customer experience in the BFSI sector.

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