"How often do consumers applying for a loan fill-in form after form only to be told they need to come into a branch for further information? The same goes for fraud where cards are temporarily blocked under threat that certain transactions are potentially of malicious intent. If indeed fraudulent, then cancel the card or block the transaction. Holds & branch visits are a symptom of manual back-office process-driven indecision – a dichotomy in today’s otherwise digitized world.

With expectations for seamless digital experiences, consumers are increasingly dissatisfied by manual & imperfect back-office operations plaguing the financial services value chain.

From terabytes of transactional data collected daily to thousands of data estates on consumer behavior, financial service giants should have the ingredients to understand risk scoring and fraud – what’s missing?

Whilst most large financial institutions have recognized the need to digitize their customer experiences to keep pace with the rapidly evolving FinTech sector, not all banks fully understand the need to leverage their data – leaving AI adoption lagging.

There seems to be two major challenges across the industry:

1. The need of back office data for front office usage
2. To end the user skills barrier

Often the claimed use of AI is more automation than intelligence, pocketed deployments of tools in the back office and a focus on a slicker front end has left a maturity gap between front and back office.

Hence the first challenge, the gap is a significant obstacle to a technology that pulls insights from data, and ultimately leads to better decision making. In many cases the core of the data sits in disparate, often back office, tech stacks, and how can organizations claim to be truly customer-centric or make good decisions without looking holistically at the data?

And so the second challenge, to fully adopt a technology such as AI, a broad user base is needed. Front and back office, technical and non-technical. This is an area where many AI solutions lag, requiring such a good working knowledge of statistics that they create a skills barrier preventing broader adoption. AI has not yet been democratized.

High impact use cases that are ripe for adoption, the low hanging fruit, serve as a great starting point. Those at the start of this article present two use cases in areas of the business that have:

• A good working knowledge of the principles needed for AI adoption
• A clear path to operationalisation
• Solid metrics to demonstrate ROI to the business

Areas with these requirements serve as a great gateway that will enable a clear path to success and the best chance of overcoming challenges. They will also help create AI champions within the business.

1. Credit Risk Scoring

Not the most instantly eye-catching, this use case offers a brilliant first step based on the value generated – imagine having the ability to bank the unbankable.

Risk scoring for hard to understand market segments, i.e. a young migrant family or solo-preneur with minimal credit history, opens up an entire new market segments and compliments upcoming challenges. Namely reduced customer loyalty and non-traditional customers. AI can be used to understand their behavior and create seamless customer journeys by offering rapid decisions to customers who under traditional models may have a high possibility of default.

2. Fraud

All organizations large or small understand their current losses due to fraud, however they don’t always get it right or understand that there is room for optimization. Fraud presents a use case with a clear metric in an area of the business already used for modeling – but the area can be supercharged through AI.

3. Trend analytics

AI can be used throughout Capital Markets, enabling teams to supercharge the analysis of real-world changes and their effect on markets, which will bring clear value when measured against existing models and the money earnt per million traded. By analyzing millions of patterns a minute, teams can leverage AI to rapidly improve or fail fast.

SparkBeyond provides an AI platform that has the ability to ask millions of questions on data per minute, which is then capable of discovering patterns a domain expert may never think to test or the human eye could never spot.

The platform’s engine is built on one of the world’s largest libraries of code, and combines clever engineering to throw functions at data and:

• Find for hidden insights by extending internal data with hundreds of external data sources
• Allows both business or technical users to find root causes, microsegments & patterns
• Provides a dynamic modeling ability that immediately operationalizes ML models and is constantly evolving as the world around it changes

Through thorough driver identification, models become explainable and high performing, which means there’s no more need for a black box. The platform does this through an interface that data scientists and business users alike can use. Hello democratization.