{
  "docId": "019dd923-5ca1-7489-b632-eed251795d29",
  "docSlug": "0bf019d2d4ffd9cd",
  "documentTitle": "Banking on AI Banking Top 10 Trends for 2024",
  "authorId": "Accenture",
  "authorName": "Accenture",
  "documentKindSlug": "consulting-deck",
  "documentKindLabel": "Consulting deck",
  "sourceTypeSlug": "strategy_consulting",
  "sourceTypeLabel": "Strategy consulting",
  "presentationDate": null,
  "orientation": "landscape",
  "aspectRatio": 1.778,
  "pageNumber": 26,
  "pageCount": 48,
  "prevPage": 25,
  "nextPage": 27,
  "slideType": "industry_trends",
  "function": "establish_context",
  "density": "overcrowded",
  "nDataPoints": 2,
  "notes": null,
  "elementsJson": [
    "headline_text",
    "action_title",
    "paragraph",
    "footnote"
  ],
  "metadataConfidence": 1,
  "imagePath": null,
  "slideHref": "/slides/019dd923-5ca1-7489-b632-eed251795d29/26",
  "deckHref": "/decks/019dd923-5ca1-7489-b632-eed251795d29",
  "deckJsonHref": "/decks/019dd923-5ca1-7489-b632-eed251795d29.json",
  "deckAnchorHref": "/decks/019dd923-5ca1-7489-b632-eed251795d29#slide-26",
  "components": [
    {
      "bbox": null,
      "kind": "callout",
      "text": "In banking, all things being equal, a 1% increase in revenue translates into a ~40 bps improvement in pre-tax ROE. A 1% improvement in cost, however, only improves ROE by ~25 bps.",
      "attrs": null,
      "subkind": null,
      "toolName": "Visual emphasis",
      "toolSlug": "visual-emphasis",
      "confidence": null,
      "componentId": "019dd951-9f60-73de-a941-09d5c0a9da31",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": null,
      "kind": "metric",
      "text": "Pre-tax ROE improvement from 1% revenue increase: ~40 bps",
      "attrs": null,
      "subkind": "primary",
      "toolName": "Quantification",
      "toolSlug": "quantification",
      "confidence": null,
      "componentId": "019dd951-9f60-73de-a941-0d1f291b2a59",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.25,
        "x": 0.64,
        "y": 0.56
      },
      "kind": "paragraph",
      "text": "In the future, AI will play a major role in bringing pricing to perfection. It will consider thousands of variables to rapidly come up with a perfect price for retail and commercial customers—either individuals or small segments with very similar needs. It will measure the outcome, feed it back into its calculations along with competitive data and other changes, and adjust in real time.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "4c31b9c2-a538-4148-bb3c-1be98fa8a2e7",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.25,
        "w": 0.25,
        "x": 0.1,
        "y": 0.48
      },
      "kind": "paragraph",
      "text": "The challenge, however, has always been to predict the impact of a price change on revenue. Economists can plot graphs showing the price elasticity of demand, but they can seldom take account of all the relevant variables and offer more than an averaged view of a customer base or market.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "7745a516-3e09-4960-9b71-a002bbfbe756",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.12,
        "w": 0.25,
        "x": 0.1,
        "y": 0.34
      },
      "kind": "paragraph",
      "text": "In banking, all things being equal, a 1% increase in revenue translates into a ~40 bps improvement in pre-tax ROE. A 1% improvement in cost, however, only improves ROE by ~25 bps.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "a6f8025c-37d5-4594-90a9-8d8a703f829c",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.35,
        "w": 0.25,
        "x": 0.37,
        "y": 0.56
      },
      "kind": "paragraph",
      "text": "In 2024 we will see the beginnings of a change in all this; a different approach to pricing and sales that could be one of the most important contributions of generative AI to corporate profitability—as well as customer value. In theory there is a perfect price for each combination of customer, product, and channel. Ideally, banks would like to price customers in increasingly smaller and smaller groups to find the perfect solution—similar to how Isaac Newton used calculus to measure the area under a curve. Unfortunately, until now, banks haven’t been able to approximate Newton’s precision as he conceived of infinitely smaller spatial figures. This has meant that, for many customers, their prices were wide of the mark.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c8714624-106d-4355-820f-6d03a0f8ebdc",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.2,
        "w": 0.25,
        "x": 0.37,
        "y": 0.34
      },
      "kind": "paragraph",
      "text": "Despite years of talk about “hyper-personalization”, banks’ pricing has always been characterized more by consistency and simplicity than the ability and willingness of individual customers to pay. What’s more, with interest rates having been stuck virtually at zero for the past 15 years, there was little benefit to be gained by improving the sensitivity of pricing.",
      "attrs": null,
      "subkind": "paragraph",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "e2694d88-757a-4813-8b09-c1751cfde9c9",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.01,
        "w": 0.01,
        "x": 0.35,
        "y": 0.45
      },
      "kind": "source-note",
      "text": "24",
      "attrs": {
        "numbered": true
      },
      "subkind": null,
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "cd31f49d-ed90-4fb2-96fe-7ecdc9c48be6",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.1,
        "w": 0.36,
        "x": 0.1,
        "y": 0.18
      },
      "kind": "title",
      "text": "Every businessperson knows that a small change in price can have an oversized effect on demand, revenue and income.",
      "attrs": null,
      "subkind": "action-title",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "9ba6b4f3-32bb-40c9-a767-cf79ae972cad",
      "frameworkName": null,
      "frameworkSlug": null
    },
    {
      "bbox": {
        "h": 0.02,
        "w": 0.18,
        "x": 0.04,
        "y": 0.06
      },
      "kind": "title",
      "text": "Trend 5 | The power of pricing",
      "attrs": null,
      "subkind": "headline",
      "toolName": null,
      "toolSlug": null,
      "confidence": null,
      "componentId": "c11067c1-ec6a-48fc-b238-7d8898445e29",
      "frameworkName": null,
      "frameworkSlug": null
    }
  ],
  "metrics": [
    {
      "metricRaw": "Pre-tax ROE improvement from 1% revenue increase",
      "numberRaw": "~40 bps",
      "numberKind": "plain",
      "actionTitle": "Every businessperson knows that a small change in price can have an oversized effect on demand, revenue and income.",
      "calloutText": "In banking, all things being equal, a 1% increase in revenue translates into a ~40 bps improvement in pre-tax ROE. A 1% improvement in cost, however, only improves ROE by ~25 bps.",
      "numberScale": null,
      "numberValue": 40,
      "metricFamily": "margin_return",
      "numberCurrency": null
    }
  ],
  "tools": [
    {
      "name": "Deductive Reasoning",
      "slug": "deductive-reasoning",
      "agent": "Architect",
      "layer": "loop",
      "matchId": "019dd95a-0bbb-77da-b1d0-9790c5cc4584",
      "evidence": "Premise 1 (small-price-big-effect) + premise 2 (revenue beats cost) → conclusion.",
      "confidence": 70
    },
    {
      "name": "Concrete Language",
      "slug": "concrete-language",
      "agent": "Storyteller",
      "layer": "slide",
      "matchId": "019dd95a-0bbb-77da-b1d0-9327df10dc36",
      "evidence": "1% revenue→~40bps ROE vs 1% cost→~25bps ROE specifics.",
      "confidence": 80
    },
    {
      "name": "Logical chain",
      "slug": "logical-chain",
      "agent": null,
      "layer": "slide",
      "matchId": "c166761f-3f84-458d-a76b-d4d61dbdf62a",
      "evidence": "In 2024 we will see the beginnings of a change in all this; a different approach to pricing and sales that could be one of the most important contributions of generative AI to corporate profitability",
      "confidence": 0.5
    },
    {
      "name": "Pyramid Principle",
      "slug": "pyramid-principle",
      "agent": "Architect",
      "layer": "slide",
      "matchId": "33029b71-c813-4e44-9fad-104fea6d63c4",
      "evidence": "In 2024 we will see the beginnings of a change in all this; a different approach to pricing and sales that could be one of the most important contributions of generative AI to corporate profitability",
      "confidence": 0.6
    }
  ],
  "frameworks": [],
  "arcBeats": [
    {
      "to": 44,
      "from": 5,
      "beatId": "019dd95a-0680-7418-8208-6c972da02fde",
      "arcName": "The Mountain",
      "arcSlug": "mountain",
      "beatName": "Rising Action",
      "beatSlug": "mountain-rising-action",
      "evidence": "Ten trends cumulatively build pressure on incumbent banks.",
      "position": 2,
      "confidence": 65,
      "parentBeatName": "Development",
      "parentBeatSlug": "development"
    },
    {
      "to": 44,
      "from": 5,
      "beatId": "019dd95a-0680-7418-8208-7acbfe02bbc4",
      "arcName": "The Triple Take",
      "arcSlug": "triple-take",
      "beatName": "The Facts (What)",
      "beatSlug": "triple-take-the-facts-what",
      "evidence": "Ten trends each present data and figures (Figs 1, 2, 7, 8).",
      "position": 1,
      "confidence": 55,
      "parentBeatName": "Setup",
      "parentBeatSlug": "setup"
    }
  ],
  "loops": [
    {
      "to": 27,
      "from": 25,
      "name": "Logic Chain",
      "slug": "01-logic-chain",
      "bestFor": "Skeptical audiences, controversial recommendations, rigorous analysis",
      "matchId": "019dd95a-07fd-712f-b772-3dfc877a507e",
      "evidence": "1% revenue ≈ 40bps ROE > 1% cost ≈ 25bps premise → AI pricing conclusion.",
      "position": 5,
      "objective": "Argue dynamic AI pricing is highest-leverage move",
      "structure": "Premise 1 (Accepted truth) -> Premise 2 (Observed fact) -> Therefore... (Inevitable conclusion)",
      "confidence": 65,
      "description": "Build an airtight chain of logic where each premise leads inevitably to the conclusion"
    }
  ],
  "imagePathAlt": null,
  "thumbSrc": null,
  "thumbSrcAlt": null,
  "locked": true
}