# Why MLOps will boost the wider adoption of AI in financial services
Author:  Pal Sinha, Barnali 
Author URL: https://financedigest.com/author/pal-sinha-barnali
Published: 2023-07-13
Category: FINANCE
Category URL: https://financedigest.com/category/finance
Meta Title: How MLOps Revolutionises AI Adoption in Financial Services
Meta Description: Discover how MLOps can help financial institutions overcome hurdles and accelerate the adoption of AI in fraud detection and customer experience enhancement.
URL: https://financedigest.com/why-mlops-will-boost-the-wider-adoption-of-ai-in-financial-serviceshtml

![undefined](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/stock-tips-1736813761982-compressed.jpg)

# **Why MLOps will boost the wider adoption of AI in financial services**

![](https://prod.superblogcdn.com/site_cuid_cm5qst7v3003gwirgwqtxn8i8/images/kris-sharma-450x448-1736813761867-compressed.jpg)

_By_ **_Srikrishna ‘Kris’ Sharma,_** _Financial Services Industry Leader at Canonical_

Machine learning (ML) and artificial intelligence (AI) are quietly transforming the way the finance sector works, spanning from identifying fraudulent activities to enhancing the overall customer experience. Current estimates indicate that around [32% of banks](https://www.businesswire.com/news/home/20220131005583/en/The-Global-AI-in-Banking-Market-Will-Grow-to-64.03-Billion-by-2030-at-a-CAGR-of-32.6-During-2021-2030---ResearchAndMarkets.com) are already utilising AI technologies, and the global AI market in banking is projected to surge to $64.03 billion by 2030.  Despite this potential, financial institutions face significant hurdles in realising the tangible advantages of implementing AI and ML on a large scale. These [challenges encompass issues such as ensuring data](https://www.financedigest.com/financial-services-and-data-how-do-we-meet-the-challenge.html "Financial services and data – how do we meet the challenge?") quality, establishing model explainability, and addressing the critical concern of AI bias. This is precisely where [Machine Learning](https://www.financedigest.com/harnessing-ai-and-machine-learning-for-fraud-detection-in-trade-finance.html "Harnessing AI and machine learning for fraud detection in trade finance") Operations (MLOps) emerges as an indispensable solution.

MLOps help firms [overcome some of the common hurdles faced when implementing](https://www.financedigest.com/a-roadmap-to-success-overcoming-challenges-in-multi-cloud-implementation.html "A roadmap to success: Overcoming challenges in multi-cloud implementation") AI, by providing a systematic approach to taking ML models to production, and maintaining and monitoring them. MLOps focuses on fostering collaboration among diverse teams involved in AI, [ensuring seamless coordination between developers](https://www.financedigest.com/how-can-we-ensure-that-africas-energy-transition-aligns-with-its-development-ambitions.html "How can we ensure that Africa’s energy transition aligns with its development ambitions?"), data scientists, and IT operations teams. For banks seeking to explore AI, MLOps establishes a reliable framework that ensures improved code quality, swift and efficient patching, and streamlined release processes. It serves as the bedrock for establishing trustworthy and scalable AI in the [financial services](https://www.financedigest.com/striking-the-balance-between-consistent-creative-and-compliant-content-for-financial-services-on-social.html "Striking the balance between consistent, creative, and compliant content for financial services on social ") sector, playing a crucial role in facilitating the rapid adoption of AI technology.

## **The battle against fraud**

Fraud poses a persistent and significant challenge for financial institutions, [demanding early detection to mitigate losses and safeguard customers](https://www.financedigest.com/lonza-cuts-2023-outlook-on-slower-growth-lower-customer-demand.html "Lonza cuts 2023 outlook on slower growth, lower customer demand"). In the United Kingdom, the impact of fraud has [returned to pre-pandemic levels](https://kpmg.com/uk/en/home/media/press-releases/2023/02/total-value-of-uk-fraud-returns-to-pre-pandemic-levels.html), with cases valued at £100k or higher surging to £1.12bn in 2022, reflecting a remarkable 151% increase from the previous year. To combat this rising threat, [banks and financial](https://www.financedigest.com/the-transformative-power-of-technology-in-banking-and-finance.html "The Transformative Power of Technology in Banking and Finance") institutions are increasingly leveraging AI-powered fraud detection models capable of analysing extensive datasets.

AI-driven [fraud detection](https://www.financedigest.com/bridging-the-gap-unravelling-the-connection-between-fraud-detection-and-aml.html "Bridging the gap: Unravelling the connection between fraud detection and aml") provides continuous monitoring of transactions and activities as they unfold in real-time. This instantaneous [analysis enables rapid identification of potential](https://www.financedigest.com/lte-and-lte-advance-market-potential-growth-share-demand-and-analysis-of-key-players-forecasts-to-2025.html "LTE And LTE Advance Market Potential Growth, Share, Demand and Analysis of Key Players – Forecasts to 2025") fraud, empowering financial institutions to take prompt action to mitigate losses and safeguard customers. Beyond detecting ongoing fraudulent activities, AI models contribute to [fraud prevention](https://www.financedigest.com/fintechs-want-fraud-prevention-taught-in-schools-as-britain-becomes-a-passwordless-society.html "Fintechs want fraud prevention taught in schools as Britain becomes a passwordless society") as well. By analysing historical fraud data and patterns, AI systems can identify potential vulnerabilities and [offer proactive recommendations](https://www.financedigest.com/germanys-slm-recommends-shareholders-accept-nikon-takeover-offer.html "Germany’s SLM recommends shareholders accept Nikon takeover offer") to strengthen security controls, minimise risks, and prevent fraud.

MLOps plays a pivotal role in facilitating the development and deployment of fraud detection models, encompassing various essential steps. With MLOps, data [science](https://www.financedigest.com/crowdsourcing-of-data-to-drive-the-life-science-market.html "Crowdsourcing of data to drive the Life Science Market") teams can streamline and automate data collection and preparation processes, optimising efficiency. Moreover, it ensures the development of models in a consistent and reproducible manner, adhering to [industry best practices for data analysis](https://www.financedigest.com/insulated-box-liner-market-2021-global-industry-analysis-size-share-growth-trends-and-forecast-to-2029.html "Insulated Box Liner Market 2021: Global Industry Analysis, Size, Share, Growth, Trends And Forecast To 2029") and feature selection. Once the model transitions into the production environment, MLOps guarantees scalable and dependable deployment, allowing teams to continuously monitor model performance, promptly identify any issues, and make the necessary changes to improve performance.

## **A personal touch**

Many [banks have made substantial investments](https://www.financedigest.com/credit-suisse-appoints-investment-banking-management-committee-memo.html "Credit Suisse appoints investment banking management committee -memo") in AI, but they often struggle to achieve a significant return on their investment. This can be attributed to several factors, including inconsistent customer data, limited knowledge sharing, and AI models with narrow scopes or limited replicability. To address this issue, [banks must improve their ability to develop a comprehensive suite](https://www.financedigest.com/why-google-is-never-closed-for-maintenance-and-how-banks-can-follow-suit.html "Why Google is never closed for maintenance and how banks can follow suit") of machine learning (ML) models capable of driving personalised engagement at every customer touchpoint.

Currently, many ML models in banking are trained on isolated moments, focusing on short-term, product-driven objectives like increasing mortgage applications or account openings. However, to truly harness the power of AI for personalisation, [banks need](https://www.financedigest.com/why-challenger-banks-need-to-capture-the-hearts-and-minds-of-todays-consumers-to-become-the-number-one-choice-in-the-retail-banking-sector.html "Why challenger banks need to capture the hearts and minds of today’s consumers to become the number one choice in the retail banking sector") to shift their focus towards identifying the drivers of customer lifetime value and shaping customer interactions based on those insights.

MLOps can help ensure that the model is [developed using best practices for data analysis](https://www.financedigest.com/automotive-mems-sensor-market-key-manufacturers-development-trends-and-competitive-analysis-2028.html "Automotive MEMS Sensor Market Key Manufacturers, Development Trends and Competitive Analysis 2028"), feature selection, and model training. In most cases, ML models and campaign-management systems often lack feedback loops to connect them, resulting in banks being unable to apply predictive insights from their ML models to inform campaign execution and decision-making. MLOps promotes collaboration between data scientists and various operations teams, helping to ensure that banks can confidently apply predictive insights to decision-making and create personalisation programmes.

## **Dealing with risk**

Credit [risk has always been a challenging area for banks](https://www.financedigest.com/the-risks-and-rewards-of-ai-in-banking-in-2022.html "The risks and rewards of AI in banking in 2022"), given the multiple factors that form an individual’s risk profile. Credit risk assessment involves analysing a borrower’s credit history, financial [statements](https://www.financedigest.com/uk-finance-minister-hunt-to-make-statement-at-1000-gmt-source.html "UK finance minister Hunt to make statement at 1000 GMT – source"), and other relevant data to determine their ability to repay a loan. AI/ML are changing the way credit risk is assessed, with ML models getting increasingly accurate with each round of training.

However, ML models may still contain assumptions that can pose a significant challenge when analysing noisy historical financial data and may lead to poor model performance. There’s also a risk of overfitting the data, as ML models are more sensitive to outliers than traditional analytics. Feature [engineering involves selecting and engineering the features used in the ML model](https://www.financedigest.com/mercedes-benz-launches-e-class-its-last-new-combustion-engine-model.html "Mercedes-Benz launches E-Class, its last new combustion engine model"). MLOps can help [banks and financial](https://www.financedigest.com/new-research-indicates-confusing-bank-jargon-could-be-fuelling-financial-vulnerability.html "New research indicates confusing bank jargon could be fuelling financial vulnerability") institutions to help automate this process and ensure that the features are selected based on their relevance to the credit risk assessment task.

Despite their potential benefits, AI/ML [models used in credit risk](https://www.financedigest.com/banks-need-better-model-risk-management-heres-how-to-build-a-robust-framework.html "Banks need better model risk management – Here’s how to build a robust framework") assessment face a critical challenge—bias. AI/ML models can inadvertently reflect biases and prejudices that exist in the data used to train them, leading to unfair or discriminatory outcomes. Addressing this issue is essential for maintaining fairness and ensuring ethical practices in lending. By leveraging MLOps, [banks can detect and address biases in the data](https://www.financedigest.com/asia-shares-edge-higher-wary-of-us-bank-data.html "Asia shares edge higher, wary of US bank data") used to train the models thereby mitigating bias and promoting fairness in credit risk assessment models. MLOps enables thorough testing on diverse and representative datasets, ensuring that the [models are not skewed towards](https://www.financedigest.com/the-dressing-vinegar-and-condiments-market-to-move-towards-healthcare-models-of-convenience.html "The Dressing Vinegar And Condiments Market To Move Towards Healthcare Models Of Convenience") any particular group or demographic. MLOps practices facilitate continuous monitoring and evaluation of the credit risk assessment models. This ongoing monitoring allows banks to identify and rectify any biases that may emerge over time, ensuring that the models remain fair and objective throughout their lifecycle.

Additionally, MLOps supports compliance efforts, enabling [banks to adhere to regulatory guidelines and promote responsible lending](https://www.financedigest.com/irish-central-bank-eases-strict-mortgage-lending-limits.html "Irish central bank eases strict mortgage-lending limits") practices.

### **Accelerating AI Adoption in Financial Services – The role of MLOps**

MLOps empowers [business leaders in the financial](https://www.financedigest.com/what-green-criteria-sustainable-finance-houses-look-for-when-conducting-due-diligence-and-how-businesses-seeking-to-attract-investment-can-get-their-house-in-order.html "What green criteria sustainable finance houses look for when conducting due diligence, and how businesses seeking to attract investment can get their house in order") services industry to effectively harness the potential of AI, instilling confidence in stakeholders and regulatory bodies alike. By embracing MLOps practices, financial institutions can address common challenges associated with AI, such as transparency, reliability, fairness, and compliance. Through process automation and streamlining, MLOps significantly reduces the time and effort required for developing, deploying, and maintaining AI models, leading to enhanced cost-effectiveness and [operational efficiency](https://www.financedigest.com/naturgys-profit-rises-on-lng-gains-soothing-shareholders-by-pietro-lombardi-madrid-reuters-spanish-power-utility-naturgy-reported-on-monday-an-88-jump-in-profit-for-the-first-six-months-of.html "Naturgy’s profit rises on LNG gains, soothing shareholders By Pietro Lombardi MADRID (Reuters) – Spanish power utility Naturgy reported on Monday an 88% jump in profit for the first six months of the year, boosted by strong earnings at its liquefied natural gas (LNG) business, which could help it to win over restive investors. Earlier this month, the company said it would increase its dividend floor through 2025 while trimming expected investment, as it sought to make rewarding shareholders a priority. Pleasing them has become a focus as Naturgy considers changes that could reshape the company and after arguments over the potential appointment of a chief executive. Naturgy’s first-half net profit jumped to 1.05 billion euros (.16 billion), boosted by its liquefied natural gas (LNG) business. Shares were down 0.2% in mid-morning trading, bucking deeper declines, in particular for Spain’s blue-chip banks and utilities after a general election on Sunday produced no clear winner. Renta 4 Banco analyst Angel Perez Llamazares said Naturgy performed better than expected, adding “cash flows also beat our expectation,” and debt fell more than expected. After natural gas prices hit record levels last year, Naturgy said a fall in procurement costs this year and hedging gains had boosed profits. In the first half of the year, the company “exceeded the objectives of operating efficiency, cash generation, investment materialisation and debt reduction,” Executive Chairman and CEO Francisco Reynes said. After rival Iberdrola named a separate CEO last year, Naturgy is the only large energy company in Spain to combine the roles of executive chairman and CEO. Earlier this month, the company raised the dividend floor to 1.40 euros a share, from 1.20 euros. Operating profit (earnings before interest, tax, depreciation and amortisation) is expected to reach 5.1 billion euros, compared with a previous guidance of 4.8 billion euros. It said it was targeting net profit of 1.8 billion euros in 2025. Naturgy also relaunched its plan to split regulated infrastructure operations and liberalised energy businesses into two listed companies. The project had been suspended after the Ukraine war disrupted energy markets and analysts are still sceptical. “We believe it is unlikely that Naturgy will be able to implement its asset split in the short term and it is also unlikely that a big change in the shareholding of the company would occur,” said RBC analyst Fernando Garcia. ( = 0.8986 euros) (Reporting by Pietro Lombardi, editing by Inti Landauro and Barbara Lewis)").

The advantages of AI adoption in the financial services sector are tangible and far-reaching, positively impacting areas such as fraud detection, credit risk assessment, and personalised [customer experiences](https://www.financedigest.com/revolutionising-finance-exploring-the-impact-of-omnichannel-insurance-on-customer-experience.html "Revolutionising Finance: Exploring the Impact of Omnichannel Insurance on Customer Experience"). With its transformative potential, AI holds the key to driving significant advancements in [financial services](https://www.financedigest.com/why-financial-services-firms-are-banking-on-behavioural-biometrics-to-beat-fraud.html "Why financial services firms are banking on behavioural biometrics to beat fraud"), and MLOps emerges as a critical catalyst in accelerating the adoption and realisation of these benefits.


---
This blog is powered by Superblog. Visit https://superblog.ai to know more.
---