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Role of RPA in Streamlining Banking, Finance & Accounting Operations

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Role of RPA in Streamlining Banking, Finance & Accounting Operations

Emerging technologies have accelerated the adoption of AI and robotic process automation (RPA) in the fintech sector. These technologies offer unparalleled opportunities across multiple banking, finance, and accounting operations.

Entrepreneurs who implement RPA and AI software into their business practices benefit from automated opportunities from financial forecasting, accurate analytics, budgeting, and efficient fraud protection.

Let’s find out in detail the role of RPA in streamlining banking, finance, and accounting operations.

Preparation of Financial Statements

Most businesses keep tracking their finances regularly to observe their revenue and expenses and make adjustments accordingly.

With RPA software, the process of preparing financial statements can be accomplished quickly and without hassle. Therefore, companies that aim to check their performance in real-time can leverage benefits by implementing the RPA process.

Invoice Automation

Invoice processing is a manual task that is time-consuming and sometimes redundant. In this process, the accounting department receives an invoice to cross-check its items with the services received.

This process includes sending the invoices to several other staff members or analyzing a series of spreadsheets. This is where RPA comes into play. By implementing RPA, businesses can automate the entire process.

With RPA, the entire process is automated. Simply put, everything can be done faster with a software bot, from cross-checking the invoices items to sending them to the relevant staff members.

Client Onboarding

When performed manually, client onboarding in banks is a long hour process. It incorporates manual verification of countless documents, the client’s background checks, and financial history.

RPA finance bot can make the process easier by capturing the data from all relevant documents using the optical character recognition technique (OCR) in a fraction of time and generating an automatic report for your compliance manager.

Later, the data automatically enters into the client’s management portal. RPA automation in client onboarding helps avoid manual errors and saves the employees time and effort required in manual processes.

Tax Reporting

When it comes to calculating a business’s taxes payable, one needs to gather the required documents and financial details for evaluation. Such a critical and tedious task may lead to errors and mistakes.

To mitigate such challenges, banking and accounting firms use the RPA process to make it easier to gather your tax data and prepare an accurate tax report based on your documentation.

Processing of Expenses

Carrying out the manual process of expenses evaluation and reimbursements are common challenges in accounting operations. It involves verifying the figures and other details to ensure accuracy before feeding the information into your system, which eventually delays reimbursements.

Integrating automation software in the expenses processes streamlines this entire operation instantly. In addition, an RPA bot can fetch the data from each expense mentioned in the form in real-time without compromising accuracy and reliability.

Credit Card Processing

Credit card application approval is one of the time-consuming processes at banks. It commonly takes an extended period to validate the customer details before approving the credit card.

With the RPA bot, banks can quickly approve/disapprove the application by implementing a rule-based approach.

Loan Processing

Loan processing is a tediously slow process. Although the banks have implemented automation opportunities in their existing operation to a certain extent, RPA further accelerates it and makes it a process of 10-15 minutes.

Financial Planning & Analysis

Forecasting short- and long-term financial strategies for the next financial year can be a daunting task. It involves in-depth research and previous year’s data across all departments to develop projected goals based on these metrics.

An RPA bot can make forecasting easier, accurate, and reliable and enable organizations to make significant business decisions for the company hassle-free by creating budget models automatically.

Further, it allows organizations to visualize an unlimited range of scenarios without the risk of human error.

Anti-Money Laundering (AML) and Know Your Customer (KYC)

Both AML and KYC are significantly data-intensive processes, making them most suited for RPA. From automating the manual processes to detecting suspicious banking transactions, RPA bots are accurate and reliable when it comes to saving time and cost compared to traditional banking solutions.

Mortgage Lending

Mortgage lending is one of the critical services in the banking and financial sector that is highly process-driven and time-consuming.

RPA offers automation opportunities that make the mortgage lending process hassle-free. It includes loan initiation, document processing, verification, financial comparisons, and quality check. As a result, the loan approval process becomes easier and quick, leading to enhanced customer satisfaction.

Frequently Asked Questions (FAQs)

What can RPA do in accounting?

Organizations can enhance productivity, reduce costs, and streamline compliance with robotic process automation. In addition, it allows more time for your team to act proactively and focus on the strategic planning that adds value to your business growth.

Why is RPA important in banking?

The primary goal of implementing RPA in finance and banking operations is to reduce repetitive processes. Simply put, RPA enables banks and financial institutions to boost their productivity by engaging customers in real-time and achieving operational efficiency with the help of software robots.

What are the benefits of using RPA?

Here are the RPA Benefits

  • Boost Productivity Across the departments.
  • Improve Efficiency to Generate Savings.
  • Ensure accuracy, reliability, and consistency in the operations.
  • Enhance business data security.
  • Seize Opportunities for Scale.
  • Produce Data for Important Analytics.
  • Deliver a better customer service experience.

Why should I consider RPA for my business?

RPA is one of the fastest-growing technologies that businesses across the industry are implementing in their operations to boost overall productivity, quality, and efficiency.

In addition, integrating RPA software in your business can accelerate your business operations and optimize speed to market by reducing the overall cost that you invest in expanding your resources and workforce. Hence, RPA is worth consideration to meet your business needs cost-efficiently.

Empower Your Banking and Accounting Operations With RPA

Today, banking and financial institutions have already started leveraging automation opportunities by implementing RPA into their operations.

RPA can accelerate banking and accounting operations accurately and quickly if appropriately implemented. Moreover, it enables organizations to enhance productivity, mitigate the risks of human errors, and ensure an impressive turnaround time.


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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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