In March last year (2023), three banks in the US went bankrupt. The most notable was Silicon Valley Bank (SVB), the second-largest banking failure in US history after Washington Mutual. The Federal Reserve and other big banks had to provide a $70 billion rescue package to prevent a systemic meltdown. These events raise fears of a return to the 2008 global financial crisis. Undoubtedly, the banking sector is in deep turmoil – can DataOps help banks out?

With DataOps, banks can accelerate their data processes, reducing the time it takes to go from data ingestion to data delivery. This makes banks more responsive to market changes, giving them an edge over competitors. In today’s article, we will explore how banks can benefit from DataOps tools such as FLIP.

 

Current Problems Facing Banks in the USA

The US banking sector has faced several challenges over the years, with the 2008 financial crisis being one of the significant disruptors. Let's take a deep dive into the state of the banking sector and the solutions offered by DataOps.

The US banking sector has faced several challenges over the years, with the 2008 financial crisis being one of the significant disruptors. Let’s take a deep dive into the state of the banking sector and the solutions offered by DataOps.

 

Regulatory Compliance

Banks must follow numerous rules and regulations to make the financial system safer and fairer. However, compliance can increase costs and risks for banks.

Low-Interest Rates

Low-interest rates have a negative impact on banks’ profits. They reduce the difference between what banks earn and pay on interest and decrease demand for loans, resulting in borrowers refinancing at lower rates.

Cybersecurity Risks

Banks face increasing cyberattacks from hackers and other malicious actors who seek to steal data and money or cause disruption. Consequently, banks must spend more on cybersecurity to protect themselves and their customers. Cyberattacks can also harm the banks’ reputation, customers, and finances.

Fintech Competition

Banks are facing heightened competition from fintech companies that leverage technology to provide better products and services to customers. Fintechs offer online lending, payments, wealth management, and other services at extremely low rates.

 

What is DataOps?

DataOps is a methodology combining DevOps, Agile development, and Lean manufacturing principles to create a data-focused approach to management. 

The key principles of DataOps include: 

  • Data quality
  • Data integration
  • Data security
  • Collaboration
  • Automation
  • Continuous improvement

 

How DataOps Benefits Banks 

DataOps can help banks better understand each customer’s context, behavior, needs, and preferences. This understanding, in turn, enables the bank to craft an intelligent, personalized offering that matches the customer’s present situation and future goals.

Customer intelligence 

DataOps can help banks better understand each customer’s context, behavior, needs, and preferences. This understanding, in turn, enables the bank to craft an intelligent, personalized offering that matches the customer’s present situation and future goals.

Customer intelligence can help banks to:

  • Improve customer satisfaction and loyalty by providing relevant, timely, and convenient solutions.
  • Increase cross-selling and up-selling opportunities by identifying customer segments, needs, and interests.
  • Enhance customer acquisition and retention by creating differentiated value propositions and experiences.

Fraud detection

DataOps can help banks to identify and prevent fraudulent transactions and activities using advanced algorithms and machine learning models. Some of the ways that banks can leverage DataOps and AI for fraud detection are:

  • Using anomaly detection to spot transactions that deviate from normal patterns or behaviors.
  • Using behavioral biometrics to verify user identity and detect impostors based on their interaction with devices or applications.
  • Using real-time scoring and decisioning to flag high-risk transactions and trigger appropriate actions or alerts.
  • Using predictive analytics and prescriptive analytics to forecast fraud trends and suggest optimal countermeasures.

Credit scoring 

Credit scoring is a statistical analysis performed by lenders and financial institutions to assess the creditworthiness of a person. It helps lenders decide whether to extend or deny credit and what terms and conditions to offer.

Some of the ways that banks can leverage DataOps for credit scoring are:

  • Analysis of traditional and alternative data sources, such as credit history, income, assets, social media, mobile phone usage, etc., and generate credit scores and ratings.
  • Extract and generate information from unstructured data sources, such as text documents, images, videos, etc., and use them for credit scoring.
  • Using predictive analytics and prescriptive analytics to forecast borrower behavior, preferences, and needs, and suggest optimal credit products or offers.

 

FLIP: Delivering DataOps for Banks & Financial Institutions

FLIP is a cloud-based platform that provides automated data integration from various sources to data warehouses. It will help data teams at your bank simplify and streamline their data pipelines. With its AI-powered zero-code interface, data processes become much easier to operate.

What makes FLIP stand out from other DataOps tools is its ML readiness and cost-effectiveness.  Machine Learning in a DataOps platform is useful for banks since they handle large amounts of customer data swiftly in batches and avoid the potential costs of additional data tools.

 

Benefits of FLIP: AI-Driven Data Analytics

FLIP is a cloud-based platform that provides automated data integration from various sources to data warehouses. It will help data teams at your bank simplify and streamline their data pipelines. With its AI-powered zero-code interface, data processes become much easier to operate.

Ease of use

FLIP has an intuitive user interface that allows users to set up and manage data pipelines in minutes. Users can choose from hundreds of pre-built connectors that cover a wide range of data sources. These include databases, applications, APIs, files, etc. Our platform handles all the complexities of data extraction, loading, transformation, and schema management behind the scenes.

Reliability

FLIP ensures high availability and performance of data pipelines, with 99.9% uptime across 1 million daily syncs. The DataOps platform also automatically handles schema changes, data normalization, deduplication, and error recovery, ensuring data quality and consistency. Further, it offers various levels of compliance and security features, such as encryption, access control, audit logs, etc.

Scalability

FLIP can handle any volume and velocity of data, with real-time data movement and low impact on source systems. It also supports various replication methods and architectures, such as incremental, full-table, and log-based, to suit different use cases and requirements. The platform allows users to customize and extend their data pipelines with SQL transformations, webhooks, functions, etc.

 

Sign up for a free 30-day trial today and revolutionize your banking business with the power of data. Give us a call today and book a demo.

FAQ

 

What is DataOps and How is it Relevant to Banking and Finance?

DataOps is a methodology that combines DevOps, Agile development, and Lean manufacturing principles, focusing on data management. It’s particularly relevant to banking and finance for optimizing data processes, ensuring data quality, and enhancing data security.

Can DataOps Improve Customer Intelligence in Banking?

Yes, DataOps can significantly improve customer intelligence. It enables banks to understand each customer's context, behavior, needs, and preferences, leading to personalized offerings and improved customer satisfaction.

How Can DataOps Help Prevent Fraud in Banking and Finance?

DataOps can help in fraud detection by using advanced algorithms and machine learning models. It enables banks to identify and prevent fraudulent transactions through anomaly detection, behavioral biometrics, real-time scoring, and predictive analytics.

What Are the Key Principles of DataOps?

The key principles of DataOps include data quality, data integration, data security, collaboration, automation, and continuous improvement, all aimed at enhancing the management and usability of data.

What Role Does DataOps Play in Enhancing Credit Scoring?

DataOps enhances credit scoring by analyzing both traditional and alternative data sources, including social media and mobile phone usage. It uses predictive analytics to forecast borrower behavior and suggests optimal credit products.

How Does DataOps Improve Data Quality in Banking?

DataOps improves data quality by ensuring that data is accurate, up-to-date, and consistent, which is crucial for making informed decisions in the banking sector.

What Is the Importance of Data Security in DataOps for Banking?

Data security is a critical aspect of DataOps in banking, ensuring sensitive financial data is protected from cyber threats, thus maintaining customer trust and regulatory compliance.

What Are the Reliability Features of FLIP?

FLIP ensures high availability with 99.9% uptime and handles 1 million daily syncs. It automatically manages schema changes, data normalization, deduplication, and error recovery, ensuring data quality and consistency.

Can FLIP Handle Large Volumes of Data?

Yes, FLIP is designed to handle any volume and velocity of data, offering real-time data movement and minimal impact on source systems. It supports various replication methods to suit different use cases and requirements.

Can DataOps Help Banks in Customer Retention and Acquisition?

Yes, by providing personalized and relevant services through improved customer intelligence, DataOps can help banks in enhancing customer acquisition and retention.

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Thank you for reading our new series of posts on FLIP. If you want to know more about Kanerika and FLIP, please write to us at contact@kanerika.com.

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